Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 248 appraisals
Sleep medicine
Concordance of wearable device sleep metrics with patient-reported sleep quality: A systematic review
BACKGROUND: Commercial wearable devices increasingly monitor sleep, but their concordance with patient-reported sleep quality remains poorly characterized. This systematic review evaluates concordance between wearable sleep metrics and validated subjective measures. METHODS: Following PRISMA guidelines, we searched PubMed/MEDLINE, Embase, and Cochrane Library through October 2025 for studies comparing consumer wrist-worn actigraphy-based devices with validated subjective sleep quality measures in adults (≥18 years). Two reviewers independently performed screening, extraction, and QUADAS-2 quality assessment. GRADE criteria evaluated evidence certainty. RESULTS: Five observational studies (2006 participants) were included. Wearable devices showed poor to moderate agreement with subjective assessments, explaining only 2.5-16.2% variance. Total sleep time moderately correlated with same-day diaries (r = 0.367), but devices failed to capture Pittsburgh Sleep Quality Index scores. Agreement varied substantially by population: good sleepers showed 82.4% concordance versus 39.4% in insomnia patients (p = 0.006). Clinical populations and older adults demonstrated poor agreement. Polysomnography concordance was also poor: sleep efficiency showed fair intraclass correlation coefficient values (0.478-0.570) with systematic overestimation (+1.75% to +7.9%), sleep onset latency correlated poorly (r = 0.033), and wake after sleep onset was underestimated (-7 to -30 min). Evidence certainty ranged from low to moderate. CONCLUSIONS: Commercial wearable sleep trackers demonstrate poor to moderate agreement with validated subjective sleep quality measures, with significant population-specific variation. Device data should complement, not replace, validated subjective assessments, as current technology inadequately captures patient-reported sleep quality and shows systematic bias in objective parameters.
3 Aug 2026
Read appraisal →Bioelectrochemistry (Amsterdam, Netherlands)
From data to diagnosis: A comprehensive review of machine learning-driven wearable sensors in healthcare.
The integration of machine learning (ML) with advanced wearable sensor technologies is revolutionizing healthcare by enabling real-time, intelligent monitoring of physiological parameters such as electrocardiogram (ECG), blood glucose, and respiratory patterns. This review systematically examines the transformative potential of ML-driven biosensors across three core domains: health monitoring, early disease detection, and precision medicine. Key technological advancements-including self-optimizing sensor networks, explainable AI (XAI) architectures, and edge-computing-enabled miniaturized devices-are critically evaluated. Despite rapid progress, the translation of these technologies into clinical practice faces significant challenges, such as data standardization, algorithmic interpretability, privacy concerns, and regulatory hurdles. This paper also discusses emerging trends, including federated learning, quantum machine learning, and neural interfaces, which hold promise for overcoming these barriers. By addressing these challenges and leveraging ongoing interdisciplinary collaborations, ML-enhanced wearable systems are poised to redefine personalized medicine and proactive healthcare delivery on a global scale.
2 Aug 2026
Read appraisal →JMIR mHealth and uHealth
Wearable Devices for Monitoring and Management of Comorbid Obstructive Sleep Apnea and Hypertension: Scoping Review
BACKGROUND: As an established driver of hypertension, obstructive sleep apnea (OSA) generates a significant cardiovascular burden when both disorders are present. This intersection not only compromises nocturnal hemodynamics but also hampers long-term clinical management. Yet, current health care delivery rarely integrates the simultaneous and continuous tracking of these dual burdens. While wearable technology provides a noninvasive, pragmatic toolset for synchronized physiological monitoring, research remains largely siloed within single-disease frameworks. Consequently, clinical evidence supporting wearable applications specifically for comorbid populations remains sparse. OBJECTIVE: In this scoping review, we summarized the current applications of wearable technology for comorbid OSA and hypertension. The analysis primarily outlines device categories, monitored physiological indicators, prevalent clinical scenarios, and existing challenges. METHODS: The search for relevant literature spanned PubMed, Web of Science, Embase, and IEEE Xplore, covering the period from January 2015 to February 2026. Eligible studies included adults and used wearable or portable technologies to objectively track sleep- or respiratory-related indicators and cardiovascular/blood pressure metrics. Study selection, data charting, and evidence synthesis were conducted using a 2-reviewer process and descriptive and narrative approaches. RESULTS: Our initial search yielded 739 records. Following title and abstract screening, we reviewed 54 full texts, ultimately finalizing a cohort of 13 eligible studies. Published between 2015 and 2026 across 9 countries, these articles capture data from 5596 participants. Most were observational studies and device validation studies. Evaluated device types included wrist-worn devices, fingertip contact devices, patch and single-lead devices, and multiparameter portable monitoring systems. The clinical applications mainly focused on screening and risk stratification for comorbid OSA and hypertension, monitoring of abnormal nocturnal blood pressure and hemodynamic changes, and cardiovascular risk assessment and remote longitudinal management. However, research remains sparse, and different devices vary greatly in reference standards, diagnostic thresholds, and validation pathways; therefore, their clinical translational value still requires further validation. CONCLUSIONS: Wearable devices may complement traditional assessment by providing continuous nocturnal and longitudinal data. However, at present, they are more suitable as auxiliary monitoring and risk warning tools in comorbidity management and cannot yet replace standard sleep studies or standard blood pressure monitoring. Future research should further shift from feasibility validation to large-sample, prospective, multicenter clinical studies in comorbid populations.
1 Aug 2026
Read appraisal →PloS one
Integrating smoking cessation into HIV care settings: A systematic review and meta-analysis of effectiveness and the evidence gap in cost-effectiveness.
The prevalence of smoking among people living with HIV (PLWH) is higher than in the general population, and PLWH who smoke are at increased risk of both smoking- and HIV-related comorbidities. As most PLWH reside in low- and middle-income countries (LMICs), there is a need for effective and cost-effective smoking cessation interventions in resource-constrained settings. We systematically reviewed and meta-analyzed the effectiveness of smoking cessation interventions for PLWH and looked for economic evaluations. Four databases (PubMed, Cochrane, Scopus, Web of Science) were searched up to March 23rd, 2026. Interventional and quasi-experimental studies evaluating smoking cessation interventions for PLWH were included. Risk of bias was assessed using Cochrane's risk-of-bias tool for randomized studies and the Effective Public Healthcare Panacea Project tool for non-randomized studies. Thirty-two articles met the inclusion criteria. Most evidence originated from high-income countries, with three randomized controlled trials conducted in LMICs (Kenya, South Africa and Vietnam). No economic evaluations were identified. Smoking cessation interventions varied in type, duration, intensity, and mode of delivery. Overall, studies had low to moderate risk of bias. GRADE assessments indicated moderate-certainty evidence that pharmacological interventions (RR 1.86, 95% CI 1.42-2.45) and tailored, intensive behavioral support (RR 1.34, 95% CI 1.05-1.71) increase smoking abstinence compared with standard care. This review indicates that pharmacological and tailored, intensive behavioral support interventions can support smoking cessation among PLWH, including emerging evidence from LMICs, but the absence of economic evaluations limits guidance for resource-constrained settings. Future research should prioritise implementation strategies and economic evaluations to support scalable integration into routine HIV care. (PROSPERO Registration no: CRD42022313630).
1 Aug 2026
Read appraisal →JAMA
Prone Positioning in Infants With Acute Bronchiolitis: The PROPOSITIS Randomized Clinical Trial
IMPORTANCE: Prone positioning has been shown to improve respiratory mechanics and oxygenation, but its clinical benefit in infants with acute viral bronchiolitis receiving high-flow nasal cannula (HFNC) support remains unknown. OBJECTIVE: To investigate whether prone positioning in infants with moderate to severe acute bronchiolitis and HFNC support reduces escalation to noninvasive or invasive ventilation. DESIGN, SETTING, AND PARTICIPANTS: Multicenter, randomized, open-label trial conducted in 15 pediatric intermediate or intensive care units in France. Infants aged 6 months or younger admitted for 24 hours or less with a diagnosis of acute bronchiolitis with moderate to severe respiratory distress requiring HFNC support were enrolled between January 2021 and November 2023 and followed up until hospital discharge (last patient discharged on December 11, 2023). INTERVENTIONS: Participants were randomly assigned to the prone position (n = 221) or supine position (n = 230). Infants in the prone position group received prone positioning for 24 hours or longer during the first 48 hours. All participants received standardized HFNC support at 2 L/kg/min. MAIN OUTCOMES AND MEASURES: The primary outcome was the need for escalation of care to noninvasive or invasive ventilation within the first 72 hours, according to prespecified criteria. Secondary outcomes included treatment failure, determined by an independent clinical adjudication committee; tolerance of prone positioning; length of hospital stay; duration of respiratory support; infant comfort; and adverse events. RESULTS: Among 451 infants randomized, 446 were included in the primary analysis (median age, 41 [IQR, 19-72] days; 54% male). Escalation of care occurred in 80 infants (17.9%), with no significant difference between the prone position (33/220 [15.0%]) and supine position (47/226 [20.8%]) groups (adjusted odds ratio, 0.66 [95% CI, 0.40-1.07]; P = .09). Secondary outcomes did not differ significantly between the 2 groups. In the safety analysis, serious adverse events occurred in 2 of 180 infants (1.1%) in the prone position group and 2 of 264 (0.8%) in the supine position group. CONCLUSIONS AND RELEVANCE: Prone positioning in infants with moderate to severe bronchiolitis receiving HFNC support did not significantly reduce escalation of care. However, the wide 95% confidence interval around the observed odds ratio suggests that this study was not definitive and further research is warranted. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03976895.
30 July 2026
Read appraisal →Histochemistry and cell biology
Molecular plasticity of LAMA3 across the disease spectrum: pathogenic mechanisms and clinical translation.
The laminin α3 chain, encoded by LAMA3, constitutes a principal component of laminin-332 (LN-332), an essential extracellular matrix (ECM) glycoprotein governing cell adhesion, proliferation, and tissue homeostasis. This systematic review consolidates current evidence on the molecular features and regulatory mechanisms of LAMA3, including its roles in PI3K/Akt, epithelial-mesenchymal transition (EMT), and Hippo-YAP signaling, as well as epigenetic and post-transcriptional modulation. Its context-dependent functions across distinct pathological states are delineated. In malignancies, including colorectal and ovarian cancers, LAMA3 functions as an oncogenic determinant that enhances invasion, metastatic dissemination, and chemotherapeutic resistance. In contrast, in hereditary diseases such as junctional epidermolysis bullosa (JEB) and chronic disorders such as idiopathic pulmonary fibrosis (IPF), LAMA3 deficiency or dysfunction constitutes a structural basis of tissue pathology. From a translational standpoint, elevated LAMA3 expression has been recognized as an independent prognostic indicator in pancreatic ductal adenocarcinoma, whereas LAMA3 promoter methylation is a candidate biomarker for platinum resistance in ovarian cancer. LAMA3-directed gene therapy for JEB has progressed to clinical evaluation. Current limitations in the field are critically examined, and emerging therapeutic approaches, including proteolysis-targeting chimeras (PROTACs), are discussed. Collectively, an integrated framework that connects LAMA3 biology with clinical applications is presented to inform future investigations and precision therapeutic strategies targeting this multifunctional molecule.
29 July 2026
Read appraisal →Physics in medicine and biology
Anatomically adaptive feature-wise linear modulation for deep learning-based low-dose CT denoising
Objective.Low-dose computed tomography (LDCT) reduces radiation dose but, introduces heterogeneous noise due to different photon attenuation based on anatomical tissue. Most deep learning techniques assume uniform noise in LDCT and perform equal noise removal across different regions, leading to sub-optimal performance across different tissues. This work aims to design a physics-based framework that explicitly models region-dependent noise characteristics to improve LDCT noise removal.Approach.We propose an anatomically adaptive noise reduction framework. The proposed anatomically adaptive feature-wise linear modulation (FiLM) model consists of a U-Net architecture that integrates two complementary units: the global FiLM unit in the encoder, which modifies features globally based on global image statistics to remove overall noise, and the local FiLM unit in the decoder, which modifies features locally based on the type of anatomical tissue. This dual design enables the modeling of overall image noise characteristics in addition to the removal of local noise associated with each anatomical tissue.Main results.The model performance was evaluated using AAPM-Mayo Clinic LDCT dataset, and the trained model tested on the TCIA dataset. The proposed model outperformed all competing methods. Local noise analysis showed that noise removal was consistent across different anatomical regions, achieving 37.14% in the lung, 48.75% in soft tissue, and 35.18% in bone. Visual results also confirmed significant improvements in noise removal, preservation of structural details, and reduction of non-residual distortions. Furthermore, the model demonstrated its ability to generalize under domain shift.Significance.This work presents a framework for anatomically adapted noise removal by linking feature modification with the physical properties of noise in LDCT through a dual-modulation process for both general and tissue-related noise. The model achieves a balance between noise removal and preservation of anatomical detail, making it a robust approach to LDCT noise removal.
28 July 2026
Read appraisal →Signal transduction and targeted therapy
Mucosal immunity and vaccine development
The mucosal system, which includes the respiratory, gastrointestinal, and urogenital tracts, serves as a primary entry point for pathogens, with a unique immune microenvironment and specialized defense mechanisms. In recent years, especially following the onset of the COVID-19 pandemic, there has been increasing recognition of the importance of mucosal immunity, motivated by an enhanced comprehension of its fundamental mechanisms. Currently, strategies based on mucosal delivery systems to administer antigens and induce strong mucosal protective immunity have become a key focus in the development of mucosal vaccines. Compared with conventional intramuscular delivery, mucosal vaccination can simultaneously elicit a robust local mucosal response, effectively block pathogen entry into the local mucosa, and generate systemic immune responses to prevent symptomatic infections and severe disease. In addition, mucosal delivery offers advantages such as ease of administration and low invasiveness, making it a more widely acceptable approach to vaccination. In the present study, we conducted a systematic review of the mechanisms of mucosal immunity, the technological platforms for mucosal vaccines, and proposed a perspective on the challenges and future directions for the development of next-generation mucosal vaccines, with the goal of enhancing public knowledge and awareness regarding mucosal immunity and its possible effects on global health.
27 July 2026
Read appraisal →Science advances
Interpretable dynamic quantitative vascular morphometry features using SHAP for anti-angiogenic therapy response prediction
Anti-angiogenic therapy benefits vary, with response rates of 40 to 70%, highlighting the need for early biomarkers to identify responders. We developed an automated machine learning framework that uses delta quantitative vascular morphometry features from standard contrast-enhanced CT to evaluate treatment response. This workflow combines automated tumor and vessel segmentation with feature extraction from routine scans for clinical use. Shapley additive explanations (SHAP)-based attributions identify key vascular and clinical features, providing meaningful, imaging-visible evidence aligned with therapy targets beyond traditional radiomics. Using baseline and follow-up CTs from 163 patients with lung cancer, we built three models using fivefold cross-validation, with the delta-merge model achieving high accuracy (area under the receiver operating characteristic curve = 0.842 internally, 0.806 externally). SHAP analysis uncovered an "arterial-dominant, venous-adaptive" pattern, where arterial involvement and venous recovery distinguish responders. This automated workflow and visualization support early, imaging-based response assessment and personalized treatment.
27 July 2026
Read appraisal →Journal of medical Internet research
Digital Peer Support Intervention for Family Caregivers of Individuals With Neuromuscular Disease: Randomized Controlled Trial
BACKGROUND: Neuromuscular diseases (NMD) affect nerves and muscles, resulting in weakness and often profound disability. Family caregivers of individuals with NMD experience significant psychological burden, stress, and reduced well-being. Digital peer support interventions may help to ameliorate these negative impacts. OBJECTIVE: This study evaluated the effect of a 12-week digital peer support intervention compared to usual care on caregiver mastery, competence, stress, burden, anxiety, and depression among family caregivers of individuals with NMD. METHODS: We conducted a parallel-group multicenter randomized controlled superiority trial in Ontario, Canada. Family caregivers of children or adults with NMD were recruited between August 2022 and September 2023 through 7 sites, social media, and national organizations. Participants were randomized 1:1 to a 12-week digital peer support intervention or usual care. The 12-week intervention comprised access to a trained peer mentor, private app-based communication via aTouchAway (Aetonix, Canada), and weekly moderated digital group discussion forums. The primary outcome was caregiver mastery measured using the Pearlin Mastery Scale, adjusted for baseline score. Secondary outcomes included caregiver stress, competence, burden, anxiety, and depression. We calculated adjusted (for baseline score) mean differences using analysis of covariance and generated multivariable linear regression models exploring associations with the intervention and caregiver age, years of caregiving, care recipient medical diagnosis, care recipient ventilation type, adjusting for baseline outcome scores. Intervention fidelity was evaluated through participant engagement metrics. RESULTS: A total of 100 participants were randomized (n=50 intervention and n=50 control). Participants had a mean age of 46.8 (SD 11.6) years, 70% (n=70) were mothers, with a mean length of caregiving of 11.8 (SD 7.6) years. We found no difference in 12-week Pearlin Mastery Scale scores (adjusted mean difference 0.67, 95% CI -1.7 to 3.1). We also found no difference in any of our secondary outcomes. Mentors and participants sent a mean of 21.3 (SD 33.3) and 17.7 (SD 33.0) messages, respectively. Overall, 62% (n=31) of participants and 92% (n=11) of mentors engaged in at least 1 program element for ≥8 of the 12 weeks. CONCLUSIONS: Our 12-week digital peer support program had no effect on caregiver mastery or other caregiving or psychological outcomes among family caregivers of individuals with NMD. This might be partly due to moderate fidelity and variability in participant engagement. Unlike prior caregiver interventions that incorporated structured psychoeducation or self-management training, this intervention evaluated primarily peer support delivered through a digital platform. This study contributes important evidence regarding the feasibility and limitations of scalable digital peer support programs for caregivers of individuals with NMD. These findings highlight the importance of intervention tailoring, participant matching, and sustained engagement. Future research should evaluate longer-duration and more individualized peer support models targeting caregivers earlier in the caregiving trajectory to improve intervention fidelity and ultimately caregiver well-being.
24 July 2026
Read appraisal →Nanotechnology
DFT and machine learning investigation of Au/Pt-decorated SnS2 monolayers for asthma and COPD diagnosis
Asthma and chronic obstructive pulmonary disease (COPD) are among the most prevalent chronic respiratory diseases worldwide, affecting hundreds of millions of people and contributing significantly to global morbidity and mortality. This work introduces a novel Au/Pt-decorated SnS2heterostructure for exhaled NO2detection, representing the new study to explore its role in lung disease diagnostics. It demonstrates ppb level NO2detection, a key biomarker for asthma and COPD, enabling early and differentiation of lung conditions by providing quantitative analysis of trace-level gases, which are often elevated in inflamed airways. While two-dimensional (2D) SnS2offers strong potential as a sensing platform, prior studies relied mainly on density functional theory (DFT) based gas sensing. Here, we present unprecedented integration of DFT and machine learning (ML) to investigate the gas sensing performance of pristine and Au/Pt-decorated SnS2monolayers. DFT analysis revealed enhanced adsorption and charge transfer upon noble-metal decoration, with Pt-SnS2showing optimal characteristics for asthma and COPD detection. Five ML models were trained on DFT and experimental-derived descriptors to rapidly predict the sensing behaviour of multiple gases, including NO2, among which XGBoost achievingR2= 0.9961. Both ML and DFT methods consistently identified NO2as the most sensitive analyte. This novel DFT-ML synergy not only validates fundamental adsorption mechanisms but also provides a scalable pathway for accelerated screening and design of high-performance gas sensors. Our findings establish a new prototype for integrating ML with first-principles simulations in the design of next-generation 2D material-based sensing devices.
24 July 2026
Read appraisal →Journal of robotic surgery
Perioperative outcomes and nodal station assessment after robotic-assisted versus video-assisted thoracoscopic segmentectomy for early-stage NSCLC: a systematic review and meta-analysis
The relative benefits of Robotic-assisted thoracoscopic segmentectomy (RATS) versus video-assisted thoracoscopic segmentectomy (VATS) for early-stage NSCLC remain uncertain. This meta-analysis compared perioperative outcomes, lymph node assessment, and postoperative safety between the two approaches. PubMed, Cochrane Library, Embase, and Web of Science were searched from inception to April 2026. Comparative studies of RATS versus VATS segmentectomy for early-stage NSCLC were included. Outcomes were pooled as weighted mean differences or odds ratios with 95% confidence intervals. Random-effects models were used for substantial heterogeneity (I² >50% or P < 0.05); otherwise, fixed-effects models were applied. Leave-one-out sensitivity plots are provided in the supplementary materials. Nine retrospective comparative studies involving 19,805 patients were included. Robotic-assisted segmentectomy was associated with a greater number of lymph node stations examined (WMD = 1.16, 95% CI: 0.51, 1.81, P < 0.001) and a shorter length of hospital stay (WMD = - 0.75, 95% CI: -1.36, - 0.15, P = 0.015). No significant differences were observed in operative time (WMD = 1.86, 95% CI: -8.80, 12.52, P = 0.732), duration of drainage (WMD = - 0.37, 95% CI: -0.79, 0.06, P = 0.090), overall complications (OR = 0.94, 95% CI: 0.67, 1.34, P = 0.745), air leak (OR = 1.02, 95% CI: 0.54, 1.91, P = 0.949), pneumonia (OR = 1.33, 95% CI: 0.89, 1.99, P = 0.170), or 30-day readmission (OR = 1.09, 95% CI: 0.94, 1.26, P = 0.257). Current retrospective evidence suggests that RATS may offer more examined lymph node stations and shorter hospital stay without increased morbidity, but these findings are limited by risk of bias and should not be interpreted as evidence of oncologic superiority.
22 July 2026
Read appraisal →Acta odontologica Scandinavica
Obstructive sleep apnea and periodontitis: a systematic review with considerations on CPAP therapy
OBJECTIVE: To evaluate the association between obstructive sleep apnoea (OSA) and periodontitis and to assess the available evidence on the effects of continuous positive airway pressure (CPAP) therapy on periodontal status. MATERIALS AND METHODS: This review was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. Comprehensive literature searches were performed in Cochrane, PubMed, Scopus, and Web of Science. Study selection and data extraction were carried out independently by two reviewers. A total of 20 original studies met the inclusion criteria. Methodological quality was assessed using the Joanna Briggs Institute critical appraisal tool. RESULTS: Fourteen of the 20 studies (70%) reported a significant association between OSA and periodontitis. However, only two studies fulfilled all methodological quality criteria, indicating an overall moderate to low quality of evidence. Data on the effects of CPAP therapy on periodontal outcomes were limited, and the available studies generally showed no significant impact. CONCLUSIONS: Current evidence supports an association between OSA and periodontitis, suggesting that OSA may contribute to periodontitis. Robust, well-designed studies with larger sample sizes and rigorous methodology are needed to clarify the nature of this relationship and to determine whether CPAP therapy influences periodontal health.
17 July 2026
Read appraisal →Journal of neurology
Effects of respiratory muscle training on respiratory function in patients with amyotrophic lateral sclerosis: a systematic review and meta-analysis
BACKGROUND: Respiratory decline is prognostically important in amyotrophic lateral sclerosis (ALS), but the efficacy of respiratory muscle training (RMT) remains uncertain. We synthesized randomized evidence on respiratory and related outcomes. METHODS: We searched databases, specialized registers, and trial registries through June 19, 2025, for randomized RMT trials in ALS. Risk of bias was assessed with the Cochrane tool. Random-effects meta-analyses reported standardized mean differences (SMDs) with 95% confidence intervals (CIs), and certainty was rated with GRADE. RESULTS: Six studies were included; five concurrently randomized trials contributed quantitative data, whereas Pinto 2013 was retained for qualitative context. RMT improved maximal expiratory pressure (MEP; SMD 0.387, 95% CI 0.192-0.581; P = 0.008) and showed small favorable effects on maximal inspiratory pressure (MIP; SMD 0.156, 95% CI 0.089-0.224; P = 0.005), sniff nasal inspiratory pressure (SNIP; SMD 0.216, 95% CI 0.039-0.392; P = 0.034), peak expiratory flow (PEF; SMD 0.205, 95% CI 0.088-0.323; P = 0.017), and ALSFRS-R (SMD 0.214, 95% CI 0.039-0.388; P = 0.030). Forced vital capacity showed a borderline favorable trend (SMD 0.129, 95% CI - 0.003 to 0.262; P = 0.053). Low heterogeneity estimates were imprecise because most endpoints included only three to four small studies. Certainty was moderate for MIP, low for MEP, FVC, and PEF, and very low for SNIP and ALSFRS-R. Exploratory analyses identified no reliable effect modifiers. CONCLUSIONS: RMT was associated with small, directionally consistent improvements mainly in pressure- and flow-based outcomes, whereas effects on FVC and longer-term clinical outcomes remain uncertain. RMT may be considered an individualized adjunct to ALS respiratory care, not a disease-modifying therapy. Larger, longer, standardized trials with patient-important endpoints are needed.
16 July 2026
Read appraisal →Sleep
Consensus roadmap on periodic leg movements during sleep: outcomes of the international PLMS taskforce
Periodic leg movements during sleep (PLMS) are a frequently observed motor phenomenon in polysomnography, yet their biological meaning and clinical relevance are debated. Further, how these should be assessed and tabulated for optimal evaluation is uncertain. To address these gaps, an international, multidisciplinary taskforce convened a series of online meetings followed by an in person taskforce workshop held in Troina, Italy, on November 14-15, 2025, where agreement was reached through iterative discussion (qualitative; no Delphi or predefined voting thresholds). This taskforce roadmap summarizes the taskforce conclusions across five priority domains: (1) clinical significance and impact, (2) measurement and characterization, (3) treatment, (4) basic and translational research, and (5) PLMS across the life span and epidemiology. For each domain, we delineate where the field currently stands, identify critical gaps in knowledge, and propose concrete strategies, including key milestones, study designs, and infrastructure needs, to advance the field in a coordinated and standardized manner. A central unifying theme is that PLMS should no longer be viewed solely as a countable polysomnographic index, but rather as a spectrum of sleep-related motor activity with heterogeneous neurophysiological substrates and variable clinical impact. Achieving progress will require harmonized scoring standards, validated automated and wearable technologies, large longitudinal and outcome-oriented studies, and sustained international collaboration. This roadmap provides a framework to guide research, clinical innovation, and future guideline development, but it is not a clinical practice guideline or standards statement.
14 July 2026
Read appraisal →Sleep
AI meets sleep surgery: assessing drug-induced sleep endoscopy interpretation with a large language model.
STUDY OBJECTIVES: To evaluate the performance and safety of a large language model in interpreting drug-induced sleep endoscopy (DISE) videos and providing treatment recommendations for obstructive sleep apnea, compared to expert human raters and the contemporaneous clinical report. METHODS: This prospective, blinded study included 16 adults undergoing drug-induced sleep endoscopy at a tertiary academic center. For each case, an anonymized procedural video, clinical vignette, and examination findings were independently reviewed by two sleep surgery experts, a senior otolaryngology resident, and a large language model. All the raters assessed the video quality, airway maneuvers, airway collapse at each anatomical site, and recommended therapy. Concordance with the clinical reference standard was evaluated using Cohen's kappa and intraclass correlation coefficients. Safety and reproducibility were assessed through subgroup and error-type analyses. RESULTS: Human raters demonstrated perfect or near-perfect agreement with the reference standard for the velum, oropharynx, tongue base, and jaw thrust response, and moderate agreement for epiglottic collapse. The large language model matched expert performance for all domains except the epiglottis, where moderate agreement was observed. Model-generated treatment recommendations were safe, consistent with accepted clinical practice, and highly reproducible between independent runs. No unsafe or discordant recommendations were identified. CONCLUSIONS: In this single-center study of 16 patients, a large language model accurately interpreted DISE videos and generated safe recommendations consistent with accepted clinical practice, approximating expert performance in most domains. Larger multicenter cohorts are needed to validate these findings and confirm generalizability. Statement of Significance This study demonstrates that artificial intelligence can interpret complex airway videos in sleep surgery and provide safe, expert-level treatment advice. By comparing the performance of a large language model with that of experienced clinicians, our findings suggest that advanced technology can help standardize decision-making in a highly subjective area of care. This work highlights the promise of artificial intelligence as an adjunct to clinical judgment, especially in settings where expert access is limited. However, important questions remain about the use of artificial intelligence in challenging cases and its integration into real-world practice. Future research should focus on larger, more diverse groups of patients and on ensuring ongoing oversight and safety.
14 July 2026
Read appraisal →PloS one
A late fusion multi-task learning for respiratory waveform and rate estimation from photoplethysmography
Continuous respiratory monitoring enables early detection of physiological deterioration, yet conventional capnography remains impractical for prolonged use. Photoplethysmography (PPG) offers a non-invasive alternative that encodes respiratory information through baseline wander (respiratory-induced intensity variation; RIIV), amplitude modulation (respiratory-induced amplitude variation; RIAV), and frequency modulation (respiratory-induced frequency variation; RIFV) of the pulsatile waveform. Existing PPG-based deep learning approaches, whether operating on the raw signal or on these physiological modulations, are limited to single-task architectures that estimate either respiratory rate or reconstruct the respiratory waveform in isolation, without jointly addressing both outputs. We propose a late fusion multi-task framework in which dedicated encoder branches independently process each modulation before fusion, and dual decoders simultaneously reconstruct the respiratory waveform and estimate the respiratory rate. The framework was evaluated on the CapnoBase (n = 42) and BIDMC (n = 52) benchmarks across multiple training strategies. For respiratory-rate estimation, the best transfer-learning configurations achieved a mean absolute error (MAE) of 2.27 bpm on CapnoBase and 1.33 bpm on BIDMC. For waveform reconstruction, the corresponding MAE values were 19.00% and 20.90%, with moderate correlations (r = 0.662 and r = 0.591, respectively). Sequential transfer learning consistently outperformed all other strategies, whereas pooled training degraded both outputs, demonstrating that capnography-derived and impedance-derived waveforms are not interchangeable training targets. These findings establish that short-window PPG can simultaneously support respiratory-rate estimation and waveform reconstruction, when reference signal compatibility is explicitly addressed in multi-task training.
12 July 2026
Read appraisal →BMJ open
Association of wearable device-based perioperative physical activity with recovery outcomes in patients undergoing lung cancer surgery: a systematic review and meta-analysis protocol.
INTRODUCTION: Lung cancer surgery is a cornerstone of curative treatment for resectable disease, but postoperative recovery is often prolonged and may be complicated by impaired pulmonary function, reduced physical activity, symptom burden and delayed functional restoration. Wearable devices may support perioperative physical activity through objective monitoring and activity promotion; however, existing evidence remains fragmented and has not been synthesised in a focused review. This protocol describes a systematic review and meta-analysis to evaluate the association of wearable device-based perioperative physical activity with recovery outcomes in patients undergoing lung cancer surgery. METHODS AND ANALYSIS: We will include randomised controlled trials, non-randomised interventional studies and prospective or retrospective cohort studies involving adults undergoing surgery for suspected or confirmed primary lung cancer. Eligible studies must examine wearable device-based perioperative physical activity during the preoperative period, postoperative period or both. The primary outcomes will be postoperative pulmonary complications, length of hospital stay and physical function or exercise capacity. Secondary outcomes will include overall postoperative complications, pulmonary function, symptom burden, health-related quality of life, chest tube duration, readmission and postoperative mortality. We will search PubMed, EMBASE, Web of Science, CINAHL and the Cochrane Library from inception to the final search date, supplemented by reference screening, forward citation tracking, grey literature and trial registry searches. Two reviewers will independently perform study selection, data extraction and risk-of-bias assessment. Where appropriate, meta-analysis will be performed using random-effects models; otherwise, findings will be synthesised narratively. ETHICS AND DISSEMINATION: Ethical approval is not required because this review will use data from published studies and publicly accessible sources only and will not involve individual participants or identifiable personal data. The findings will be disseminated through publication in a peer-reviewed journal and presentation at relevant academic or professional conferences. PROSPERO REGISTRATION NUMBER: CRD420261375254.
11 July 2026
Read appraisal →PloS one
Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis
Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis presents significant challenges for treatment and control of the disease. Resistant strains of Mycobacterium tuberculosis arise from specific mutations in the bacterium. Identification and characterization of resistance-associated mutations are crucial for effective treatment strategies because the first- and second-line drugs for the disease target distinct genes in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid Resistance, Multidrug Resistance, and Pre-extensively Drug-Resistant tuberculosis. The NIAID-NIH TB portal is a publicly available dataset of tuberculosis patients, including drug-resistance information. Our study analyzed 3,065 cases of drug-resistant TB. Eight supervised ML algorithms were implemented for the study. A Random Forest classifier with 10-fold cross-validation shows higher predictive performance than the other seven algorithms considered for further analysis. Significant drug resistance mutations were identified using SHapley Additive exPlanations feature importance. The World Health Organisation mutation catalogues, considered the gold standard for drug-resistant mutations, were used to evaluate prediction results. Mutations not reported in the WHO catalogues were identified during the post-prediction comparative analysis stage, as they may represent potential resistance-conferring markers warranting further investigation, including structural and functional validation or experimental validation. The mutations are rpoB-I480T, rpoC-G332R, L527V, gyrA-D94V, KatG-G99E, A106V, W191R, W328C, T380I, and M420T. The study further checks the stability and pathogenicity of the mutations using computational tools, including I-Mutant 2.0 and PredictSNP. The findings added more clarity and further evidence for the significance of the mutation, based on its contribution to drug resistance.
11 July 2026
Read appraisal →PloS one
Air quality index prediction using machine learning regression models: A comparative analysis
Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people's health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 µm) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). This research's novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.
10 July 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis
BACKGROUND: Since 1992, the incidence of lung cancer has shown an overall downward trend, with survival rates significantly improving in recent years due to the application of immune checkpoint inhibitors (ICIs). Assessing programmed death ligand 1 (PD-L1) expression status in lung cancer patients is crucial for predicting the efficacy of ICIs and formulating treatment plans. Therefore, there is an urgent need to assess PD-L1 expression status in lung cancer patients, particularly those with nonsmall cell lung cancer (NSCLC). While numerous studies have demonstrated promising results using artificial intelligence (AI) models based on radiomics, current understanding in this area remains incomplete. METHOD: We conducted a search of medical databases and screened studies that meet the criteria. We performed feature extraction and data extraction from these studies, analysed them using R software, and generated forest plots. We conducted subgroup analyses based on imaging modality and algorithm model. RESULTS: A total of 35 studies were included in the analysis. For the Tumor Proportion Score (TPS)1 group, the pooled area under the curve (AUC) for the validation set was 0.782. For the TPS50 group, the pooled AUC for the validation set was 0.798. Subgroup analysis revealed that machine learning performed better when predicting TPS50, while deep learning was superior for predicting TPS1. When predicting TPS, prediction models constructed using 2-fluoro-2-deoxy-d-glucose (18F) positron emission tomography/computed tomography (CT) and contrast-enhanced CT outperformed those based on noncontrast CT. Conversely, when predicting a TPS, the noncontrast-CT-based prediction model significantly outperformed the other two groups. CONCLUSIONS: Imaging modality and algorithm model are key factors influencing AI prediction models for PD-L1 expression in NSCLC patients.
10 July 2026
Read appraisal →Journal of medical systems
Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance
Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medical value for scalable sleep health management. This systematic review synthesizes methodological frameworks and performance outcomes of automatic sleep staging using cardiorespiratory signals. Four databases were searched and a total of 35 studies published since 2010 were identified. The analysis revealed that cardiorespiratory signal-based sleep staging achieved a practically meaningful accuracy of 70%, with no significant performance differences observed among signal modalities (cardiac signals, cardiorespiratory signals, or cardiac/cardiorespiratory signals combined with other non-EEG modalities) or between modeling algorithms (traditional machine learning vs. deep learning). However, we identified significant methodological heterogeneity and several critical model failure modes that hinder clinical translation, including the widespread lack of external validation, consistently poor classification of the N1 sleep stage, and limited generalization across diverse patient populations. To realize the technology's potential, future research must establish consensus-driven methodological guidelines and rigorously validate algorithms on large, demographically and clinically diverse datasets. These advances are essential for integrating cardiorespiratory-based sleep staging into healthcare systems as a scalable tool for population-level screening, longitudinal monitoring, and tiered clinical decision support.
8 July 2026
Read appraisal →Journal of medical economics
Evaluation of nirmatrelvir/ritonavir treatment for COVID-19 on health-related outcomes: a global economic value systematic literature review.
AIMS: Nirmatrelvir/ritonavir (NMV/r)i is an antiviral drug indicated for the treatment of patients with mild to moderate coronavirus disease 2019 (COVID-19) who are at high risk of progressing to severe disease. We performed an updated economic systematic literature review (eSLR) of NMV/r to build upon evidence reported in our previous eSLR and additionally examine the health-related outcomes associated with NMV/r and any potential effect of long COVID. METHODS: A systematic search of Embase, PubMed, Cochrane, and EconLit and of conference and health technology assessment agency websites was performed to identify economic analyses published between January 2022 and October 2025. RESULTS: Of the 33 included economic evaluations, most were cost-utility analyses (n = 16) and used an analysis of a short-term decision tree with a long-term Markov model (n = 11). Several types of health-related endpoints were reported by studies, with most including hospital-related endpoints (n = 20), quality-adjusted life-years (QALYs) (n = 18), and death-related endpoints (n = 18). Positive health-related outcomes were associated with NMV/r treatment, including reductions in hospitalizations and deaths and an increase in QALYs. NMV/r treatment was also associated with a reduced number of patients with long COVID complications. LIMITATIONS: Most studies were conducted in high-income countries, were based on different COVID-19 variants, and were limited in data on the long COVID population. CONCLUSION: In addition to monetary benefits, NMV/r provides short-term and long-term health-related benefits to patients with mild to moderate COVID-19. This study provides further support for NMV/r as a cost-effective treatment option.
7 July 2026
Read appraisal →Journal of epidemiology
Comparison of Influenza Epidemic Trends Based on a Large-scale Claims Database and National Infectious Disease Surveillance in Japan
BACKGROUND: Seasonal influenza is a recurrent respiratory infection, and timely detection is essential for public health. In Japan, surveillance is conducted through sentinel medical institutions under the National Epidemiological Surveillance of Infectious Diseases (NESID). Recently, access to large claims databases, such as the JMDC claims database (JMDCdb), has increased. While both are sample-based systems, JMDCdb covers a much larger population. We aimed to assess consistency between these sources in estimating influenza cases and the effective reproduction number (Rt) and to explore their utility in epidemic analysis. METHODS: We analyzed data from week 36 of 2016 to week 35 of 2019. Influenza cases were estimated from NESID (reported cases and cases per sentinel) and JMDCdb (cases with influenza-related diagnoses and antiviral prescriptions). Daily infection counts were derived to estimate Rt. RESULTS: Although minor differences appeared at epidemic peaks, estimates from NESID reports aligned well with JMDCdb. Estimates based on cases per sentinel were lower. Rt values were consistent across data sources. Rt exceeded 1.0 when cases per sentinel surpassed 0.2-0.3. Using a threshold of 0.25 cases per sentinel enabled detection of epidemic onset 4-5 weeks earlier than current standards. CONCLUSION: Claims data, such as those from JMDCdb, may be useful for retrospective examination of influenza trends. Moreover, a detailed analysis of the number of cases reported per sentinel suggested the potential to propose threshold values that enable earlier prediction of epidemics than conventional criteria.
7 July 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
Which nocturnal hypoxemia parameters are associated with ambulatory blood pressure monitoring in adults with obstructive sleep apnea? A systematic review.
OBJECTIVE: Systemic arterial hypertension affects up to 50% of patients with obstructive sleep apnea (OSA) and represents one of the main modifiable cardiovascular risk factors. Hypoxemia is a recognized marker of OSA severity; however, it remains unclear which specific desaturation parameters during sleep, such as oxygen desaturation index (ODI), mean and minimum oxygen saturation (SpO2), percentage of sleep time with peripheral oxygen saturation < 90% (T90%) or hypoxic burden (HB) are most consistently associated with elevated blood pressure (BP). This systematic review aims to evaluate, in patients with OSA, the impact between nocturnal desaturation parameters and elevated BP as measured by ambulatory blood pressure monitoring (ABPM). METHODS: A literature search was performed in PubMed, Web of Science, Scopus and Embase databases, covering all records available up to June 2025. RESULTS: A total of 5,587 records were identified through database searches, and 24 studies met the inclusion criteria. The studies revealed heterogeneous results, with some showing significant associations between hypoxemia indices and hypertension, while others did not. Evidence suggests that ODI3% is commonly related to increased BP, whereas minimum SpO2, mean SpO2 and T90% yielded variable findings. HB was evaluated in only one study, suggesting a potential role in BP reduction after CPAP; however, the available evidence remains preliminary. CONCLUSION: ODI3% showed the most consistent association with increased BP across studies, whereas other hypoxemia indices yielded more variable results. Evidence regarding HB remains preliminary and warrants further investigation. These findings suggest that although ODI currently appears to be the most reliable marker, evidence from a single study indicates that composite indices such as HB may represent more informative predictors of BP outcomes and deserve further investigation in future studies.
5 July 2026
Read appraisal →European journal of radiology
Deep learning-based segmentation and quantification of pulmonary masses on apparent diffusion coefficient maps: a multicentre reproducibility study
OBJECTIVES: To develop and validate a deep learning model for automatic segmentation of pulmonary masses on apparent diffusion coefficient (ADC) maps and to assess repeatability of automated ADC quantification. METHODS: We proposed ADCSegNet, a deep learning model tailored to pulmonary ADC maps, trained and tested on ADC maps from centre 1 (303 MRI examinations) and externally evaluated on datasets from centre 2 (70 examinations) and centre 3 (12 examinations) for generalisability. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC). Agreement between model-derived and manual ADC measurements (senior and junior radiologists) was examined using Bland-Altman analysis, the intraclass correlation coefficient [ICC(2,1)], and the reproducibility coefficient (RDC) following QIBA profiles. Repeat-scan stability was assessed on an independent prospective test-retest dataset (22 × 2 examinations) using Lin's concordance correlation coefficient (CCC). RESULTS: ADCSegNet achieved DSCs of 0.843 internally and 0.701 externally, outperforming nnU-Net and other contemporary backbones. Agreement was highest between the model and the senior radiologist; for mean ADC, the mean difference was 0.00 (95% limits of agreement, -0.13 to 0.13) × 10⁻3 mm2/s, exceeding model-junior and inter-radiologist agreement. ICC(2,1) exceeded 0.75 for most ADC metrics, and RDC confirmed high reproducibility for key metrics (mean ADC RDC%: 12.98% for model-senior vs. 22.87% for senior-junior radiologist). Test-retest analyses showed higher repeatability of ADCSegNet than radiologists for core metrics such as mean ADC (CCC 0.931 vs. 0.908). CONCLUSIONS: ADCSegNet enables accurate segmentation of pulmonary masses on ADC maps and provides reproducible automated ADC quantification, outperforming state-of-the-art backbone models while showing strong agreement with expert readers and stable performance on repeat scans.
4 July 2026
Read appraisal →The Journal of international medical research
Diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma in Chinese patients: A systematic review and meta-analysis
ObjectiveThis systematic review and meta-analysis evaluates the diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in Chinese patients with lung adenocarcinoma.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and prospectively registered in the International Prospective Register of Systematic Reviews (CRD420251273027), a systematic search of PubMed, Embase, Web of Science, the Cochrane Library, Scopus, China National Knowledge Infrastructure, Wanfang, VIP, and Chinese Biomedical Literature Database was conducted from inception to 31 October 2025. Two reviewers independently screened studies, extracted data, and assessed bias using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random-effects model was used to synthesize the data. Subgroup analyses were conducted for three factors: (a) imaging modality (computed tomography vs. positron emission tomography-computed tomography); (b) algorithm type (deep learning vs. conventional machine learning); and (3) validation strategy (external vs. internal).ResultsThirteen studies encompassing 6628 patients were included. The pooled sensitivity was 71% (95% confidence interval: 68-74), the pooled specificity was 81% (95% confidence interval: 78-84), and the summary area under the curve was 0.85 (95% confidence interval: 0.82-0.88). Deep learning models significantly outperformed conventional machine learning models (area under the curve: 0.871 vs. 0.798; P = 0.012). Computed tomography-based models yielded higher accuracy than positron emission tomography-computed tomography-based models (area under the curve: 0.879 vs. 0.828; P = 0.038). Models validated on independent external cohorts demonstrated superior performance compared with those relying solely on internal validation (area under the curve: 0.922 vs. 0.841; P = 0.006). Imaging modality was a significant source of heterogeneity (P < 0.05). No threshold effect or publication bias was detected.ConclusionMachine learning-based radiomics models exhibit promising diagnostic accuracy for the noninvasive prediction of epidermal growth factor receptor mutations in Chinese patients with lung adenocarcinoma. Computed tomography-based deep learning models subjected to independent external validation represent the current optimal approach. However, the retrospective nature and substantial heterogeneity of the included studies necessitate large-scale, prospective, multicenter trials with standardized workflows before clinical translation.
4 July 2026
Read appraisal →The Nigerian postgraduate medical journal
The Role of Digital Therapeutics and Artificial Intelligence in Chronic Disease Management: A Narrative Review
We conducted research showing that chronic disease management continued to challenge healthcare systems, payers, and patients. At the same time, digital therapeutics emerged as a promising and potentially transformative approach. They supported better management of long-term conditions, improved patient outcomes, and helped streamline healthcare delivery. We carried out a comprehensive literature search to identify both original reports and reviewed publications. The search covered multiple databases, including Google Scholar and PubMed, and we also gathered relevant information from credible online sources such as the World Health Organization and India's National Crime Records Bureau. Using these findings, the narrative explained how digital therapeutics reshaped chronic disease management by highlighting key benefits, the technologies that enabled these solutions, and the expected impacts on both clinical and economic outcomes. We also discussed the obstacles the sector encountered as it developed, and we considered the future prospects of digital therapeutics in chronic care. In conclusion, digital therapeutics offered personalised care, improved patient engagement and adherence, provided real-time monitoring and feedback, enhanced accessibility and convenience, and were cost-effective in managing chronic disease; however, challenges such as cybersecurity concerns, reliability of data, the digital divide, and a lack of extensive clinical validation needed to be addressed for widespread adoption. While the evidence to date suggested clear clinical and economic promise, realizing that promise required coordinated action stronger clinical trials to build robust evidence, clear regulatory pathways to ensure safety and efficacy, investment in secure interoperable infrastructure, and targeted efforts to close the digital divide so vulnerable populations were not left behind; policymakers, clinicians, payers, and technology developers had to collaborate to translate innovation into equitable, scalable care improvements.
3 July 2026
Read appraisal →Langmuir : the ACS journal of surfaces and colloids
Wearable Sleep Detection System Based on Piezoelectric Signals and Convolutional Neural Network Analysis
Long-term and comfortable sleep monitoring is a core requirement for daily health management. However, existing sleep monitoring technologies fail to simultaneously achieve accurate sleep stage classification, wearing comfort, and direct monitoring of characteristic physiological signals. Here, we report a wearable sleep monitoring system (SMS) based on piezoelectric sensing and convolutional neural network (CNN) analysis. The system assesses sleep structure and quality by capturing characteristic eye movement signals and provides actionable recommendations for long-term sleep health management. The sensor employs a highly stereoregular polyacrylonitrile (PAN) composite fiber film modified with PDA@ZnO as the sensing unit, providing high sensitivity, excellent wearing comfort, and long-term durability. It conforms closely to the upper eyelid, enabling effective acquisition of direct physiological signals associated with sleep. Furthermore, we developed a CNN model for five-state sleep classification and validated it using original-recording-level 5-fold validation and a 1000-sample processed held-out test set, achieving an accuracy of 95.90% with a Cohen's Kappa coefficient of 0.949. The system efficiently presents sleep quality information through a simple and user-friendly human-computer interaction interface. The proposed system introduces a novel approach to convenient health monitoring and provides a feasible technical solution for accurate and long-term sleep health assessment in daily life.
1 July 2026
Read appraisal →ACS sensors
Machine Learning-Enabled Gas Sensor Based on MOF-Derived In2O3-CuO for Exhaled CO Detection
Noninvasive, precise breath analysis holds significant importance for the screening and monitoring of neonatal jaundice, with its core challenge lying in the specific detection of the key biomarker carbon monoxide (CO) within complex breath matrices. This research employed a controlled process to fabricate five sets of In2O3-CuO sensors derived from bimetallic metal-organic frameworks (Bi MOFs). These were assembled into a microsensor array, and by incorporating a linear discriminant analysis algorithm, an electronic nose system was constructed. This approach utilizes thermodynamic feature engineering to enhance data validity and optimize algorithm selection, thereby reducing reliance on large-scale data and computational resources. By integrating thermodynamically feature-driven machine learning, the electronic nose system-comprising merely five In2O3-CuO sensors-ultimately achieved accurate discrimination between CO and acetone. Concurrently, the single 3In2O3-CuO sensor exhibits excellent reproducibility, moisture resistance, and stability, with a low detection limit of 1 ppm and relatively rapid response/recovery times (5/21 s). By introducing machine learning algorithms to analyze the multidimensional response signals from the sensor array, the study successfully addressed the cross-sensitivity issue between CO and coexisting interfering gas C3H6O in clinical environments, achieving qualitative identification of CO ranging from 1 to 50 ppm (cross-validation accuracy reached 82%), providing a highly reliable technical platform for noninvasive screening of neonatal bilirubin metabolic abnormalities.
29 June 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Mucoactive agents in bronchiectasis: a systematic review and meta-analysis
BACKGROUND: Mucoactive agents aim to improve mucociliary clearance in bronchiectasis, disrupting the cycle of impaired clearance, infection and inflammation. Registry data show 28% of patients use these agents, but prescribing varies due to limited evidence. We aimed to update evidence on mucoactive agents' effects in adults with bronchiectasis. METHODS: We conducted a systematic review and meta-analysis, searching Medline, Embase, CENTRAL and trial registries to 1 October 2025. Eligible studies included adults with bronchiectasis, evaluating any mucoactive agent versus placebo or control. Cystic fibrosis and paediatric studies were excluded. The primary outcome was exacerbation frequency; secondary outcomes included lung function, quality of life and adverse events. FINDINGS: From 1512 records, 24 studies (20 randomised, four observational: n=7051) evaluated eight mucoactive agents (ambroxol, bromhexine, carbocisteine, erdosteine, hypertonic saline, mannitol, N-acetylcysteine, rhDNase). Most trials had high or some risk of bias. Seven randomised studies (n=1259) reported exacerbations; pooled analysis did not demonstrate a statistically significant difference in annualised exacerbation incidence (mean difference -0.40 per patient per year, 95% CI -1.04-0.24; p=0.22; I2=91%; very low certainty). Nine studies (n=767) reported percent predicted forced expiratory volume in 1 s (FEV1 % pred), showing a small mean increase of 3.23% (95% CI 0.31-6.15; p=0.03; I2=69.9%; very low certainty). Pooled analyses did not demonstrate significant differences for other spirometry measures, quality of life or adverse events. INTERPRETATION: Pooled evidence did not show a reduction in exacerbations and FEV1 % pred improvements were small. Evidence certainty was low/very low, meaning overall clinical benefit remains uncertain, highlighting the need for targeted trials of specific agents and subgroups.
25 June 2026
Read appraisal →Reviews on environmental health
Air quality and public health co-benefits of national transport policies
INTRODUCTION: Transport is a major contributor to urban air pollution and premature mortality worldwide. This systematic review evaluates the air-quality and public health co-benefits of national and regional transport policies aimed at reducing population exposure to air pollution. CONTENT: Following PRISMA 2020 guidelines, this review synthesized evidence published between 2011 and 2024 on the health and environmental impacts of national or regional transport policies. Ten eligible studies were reviewed, covering diesel vehicle phase-outs, inspection and maintenance programs, electric vehicle incentives, fuel taxation, and promotion of public or active transport. SUMMARY: Across multiple settings, these policies were associated with reductions in PM2.5 concentrations and with avoided premature mortality, respiratory and cardiovascular hospitalizations, and healthcare costs. The largest and most consistent health benefits were observed for integrated policy packages, particularly those combining regulatory and technological measures such as fuel quality improvements, vehicle electrification and diesel phase-out, complemented by behavioral or fiscal instruments. Most available evidence originates from high-income countries, while studies from low- and middle-income regions remain scarce. The limited number of eligible studies and their concentration in specific regions and modeling approaches constrain generalizability, especially to settings with different transport systems, resource availability, and air-quality profiles. OUTLOOK: Embedding health-impact assessment within transport planning and expanding long-term evaluations in low- and middle-income countries are essential to ensure equitable and sustained air-quality and health improvements. Future policy frameworks should prioritize source-oriented regulatory and technological measures such as vehicle electrification, fuel-quality regulation, and diesel phase-out implemented within integrated policy packages to maximize public-health co-benefits.
25 June 2026
Read appraisal →International immunopharmacology
Antimicrobial peptides in malaria and tuberculosis management: a systematic review of emerging evidence
AMPs (Antimicrobial peptides) are small molecules that are crucial components of biological activities, viz wound healing, angiogenesis, antimicrobial activity, immune regulation, and exhibit anticancer properties. This systematic review summarizes the origin, sequence, efficacy, and structure-activity mechanisms of AMPs against tuberculosis and malaria, the world's leading killer infectious diseases, employing the AMP database (APD3) (till June 2025) and PubMed search (January 1965 to June 2025). The comprehension is well addressed by covering the inhibitory activity, SAR (structure-activity relationships), and action mechanisms of AMPs (natural and synthetic) against Mycobacterium tuberculosis and Plasmodium spp., as both pathogens exhibit multidrug resistance. The review also illustrates how these specific motifs, along with their properties such as hydrophobicity, charge, or amphipathicity, influence the AMPs activity against the pathogens. Moreover, challenges in the AMPs use and the way forward, including the recent breakthroughs employing nanocarriers, inhalable formulations for TB, and targeted erythrocyte delivery for malaria, are also highlighted. AMPs have illustrated promising anti-TB and antimalarial efficacy owing to their unique structure and novel mode of action, despite exhibiting varying extents of toxicity. Anti-malarial AMPs are grouped in defensins and cecropins, while the majority of natural anti-TB AMPs are classified as bacteriocins, defensins, and cathelicidins. Synthetic AMPs, in contrast, are designed to enhance activity and selectivity while reducing toxicity and the development of resistance. AMPs have α-helical, β-sheet, or random-coil structures and are cationic, amphipathic, and highly hydrophobic, attributes that contribute to observed anti-TB and anti-malarial potential. A comprehensive understanding of AMP's structure and function, along with the use of artificial intelligence (AI) and machine learning, can provide impetus for AMP therapy as an alternative to tackle tuberculosis and malaria.
15 June 2026
Read appraisal →Pulmonology
Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography
BACKGROUND: Traditional sleep metrics from polysomnography are often limited by the hypnogram, in which each 30-s epoch is classified into one of the five sleep stages. This oversimplifies sleep stage continuity and fragmented sleep, typical in sleep-disordered breathing (SDB). Deep learning offers more dynamic sleep modelling with a higher temporal resolution by providing probabilities for each sleep stage, known as hypnodensities. RESEARCH QUESTION AND METHODS: We investigated whether high-frequency (1/s) hypnodensities captured from nocturnal fingertip photoplethysmography (PPG) recordings better correlate with patient-reported outcomes in SDB than traditional metrics. RESULTS: Among 2,280 patients (65% male, mean age 62 years, mean apnoea-hypopnoea index (AHI) 23.5/h), we found that several PPG-derived hypnodensity metrics correlate with self-reported daytime fatigue and sleepiness. The level of correlation was better or similar compared to traditional hypnogram-based sleep metrics derived from manually scored polysomnography. Specifically, PPG-based hypnodensities for N1 and N3 sleep showed a stronger association (p < 0.005) with fatigue and sleepiness than manually scored N1 and N3 sleep. Multivariable logistic regression (adjusted for age, sex, body mass index, AHI, and total sleep time below 90% blood oxygen saturation) revealed significant associations (p < 0.01) between multiple PPG-based hypnodensity metrics and daytime sleepiness. CONCLUSION: These findings suggest that PPG-based hypnodensities improve the characterisation of sleep quality and better predict patient-reported outcomes in SDB patients. Future uses may include tracking sleep quality with fingertip pulse oximeters outside of sleep centres, potentially over multiple nights due to their simplicity.
14 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Feasibility and acceptability of ExerciseGuideUK for those living with and beyond lung cancer: a mixed methods study
PURPOSE: Digital health interventions offer promise for addressing unmet needs in individuals living with and beyond lung cancer (LWBLC). ExerciseGuideUK, a personalised, web-based exercise platform, was developed for those LWBLC. This study aimed to evaluate ExerciseGuideUK. METHODS: Participants were recruited from Hull University Teaching Hospital and used ExerciseGuideUK over eight weeks. Feasibility and acceptability were assessed using quantitative measures, including the System Usability Scale, structured questionnaires, and qualitative interviews (guided by the Theoretical Framework of Acceptability). A novel mixed-methods integration approach called the Pillar Integration process (PIP) was used to integrate quantitative and qualitative findings on feasibility, engagement, barriers, and adaptation potential. RESULTS: Eighteen participants (mean age 65 ± 14.42y) enrolled. Recruitment and retention rates were 30.5% and 77%, respectively. Notably, 25.4% of those screened were excluded due to digital access issues, including some participants who owned smartphones, suggesting limited digital literacy. Among those who participated, acceptability, usability, and engagement were high. Valued features included goal-setting tools, breathlessness management strategies, and personalised exercise prescriptions. Barriers included treatment side effects, work responsibilities, and low confidence with technology. CONCLUSION: ExerciseGuideUK showed moderate feasibility and high acceptability among users. This is the first known application of PIP in the context of lung cancer, exercise, and digital health. The PIP facilitated a comprehensive understanding of feasibility and acceptability, guiding informed adaptations by users. The PIP warrants further testing and refinement in relation to optimising digital intervention design that promotes equitable, patient-centred care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05121259.
14 June 2026
Read appraisal →Critical care (London, England)
Large language models for optimizing clinical trial recruitment in ICUs: application to ventilator-induced diaphragm dysfunction
BACKGROUND: Ventilator-induced diaphragm dysfunction (VIDD) is a frequent and under-recognized consequence of prolonged mechanical ventilation in intensive-care unit (ICU) patients. Identifying eligible candidates for clinical trials targeting VIDD remains a major operational challenge. This study evaluates the use of large language models (LLMs) to automate patient prescreening from ICU discharge summaries and estimate recruitment capacity for a future phase 2 trial. METHODS: We developed an LLM-based prescreening pipeline to assess trial eligibility criteria from ICU discharge summaries, which was deployed to screen all 2024 ICU stays. Stays that were flagged as potentially eligible underwent expert adjudication. An enriched set of 50 ICU stays was independently annotated by six clinicians to define a reference standard, which was used to evaluate criterion-level model performances using F1-scores. RESULTS: The best-performing model was GPT-OSS:120B with a criterion-level F1-score of 0.82. When applied to consecutive 1,342 ICU stays from Montpellier University Hospital in 2024, the selected model identified 532 patients with ≥ 3 days of mechanical ventilation. After applying exclusion criteria, 185 patients remained potentially eligible. Expert review confirmed 133 patients as eligible, resulting in a positive predictive value of 72% (95% CI 65-78). The LLM-assisted workflow resulted in an estimated 86% reduction in clinician review time. The LLM-based prescreening pipelines achieved criterion-level F1 scores ranging from 0.73 to 0.82, with GPT-OSS:120B demonstrating the highest performance. CONCLUSIONS: LLM-based prescreening offers a promising approach for identifying trial candidates in critical care, prioritizing candidates for clinician review. Future deployments should include targeted expert validation and ongoing monitoring to ensure safety and generalizability.
13 June 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review
Histological analysis is a cornerstone of pre-clinical respiratory disease research. It enables assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows. To date, its use in pre-clinical respiratory models has not been synthesised.We conducted a scoping review following the Joanna Briggs Institute guidelines. We searched MEDLINE and Embase (inception - January 2025) for pre-clinical studies using AI to analyse histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate.Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Haematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Pre-processing methods (e.g. stain normalisation) were common, but annotation and training practices were inconsistently reported. AI model performance was generally high (accuracy ≥90%; seven studies); however, validation metrics varied, and external validation was absent. Most studies used "black box" models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported.AI tools are poised to transform histological analysis in pre-clinical respiratory research. The field will be able to further harness AI to automate pre-clinical respiratory histological analysis by addressing gaps that we have identified in validation, transparency, and standardisation.
12 June 2026
Read appraisal →Global health action
mHealth-supported tuberculosis contact investigation by community health workers in high-burden countries: a scoping review
Mobile health (mHealth) implementation strategies in tuberculosis (TB) contact investigations are intended to increase community health workers' (CHWs) engagement in TB contact investigations and case detection. This scoping review aimed to map and synthesize existing evidence on the design and development of acceptable and feasible mHealth implementation strategies for CHW-led TB contact investigations. We conducted a scoping review following the Arksey and O'Malley framework and PRISMA-ScR guidelines. We searched PubMed, Scopus, Cochrane Library, Web of Science, and Google Scholar for English-language studies published through October 2024 on mHealth interventions for TB contact investigation by CHWs. Eligible studies included empirical studies conducted in high-burden countries (HBCs) for TB, HIV-associated TB, and MDR/RR-TB. Studies were screened, and data were extracted using Rayyan AI-assisted software. We mapped implementation strategies, barriers, facilitators, acceptability, and feasibility of mHealth interventions. Quantitative findings were described, and qualitative data were synthesized thematically. Nine studies were included. Most interventions used hybrid mHealth technologies for communication, case registration, health records, and decision support. Implementation strategies specified actors, actions, targets, temporality, dose, and justifications. Barriers were related to CHWs, digital systems, service implementation, and TB-affected persons. Facilitators included stakeholder support, CHW training, mHealth design, community engagement, and patient-centered approaches. Overall, CHWs perceived mHealth interventions as acceptable and feasible. Successful implementation in HBCs requires strengthened CHW capacity, reliable technology, optimizing interaction between CHWs and digital tools, and continuous monitoring and evaluation throughout implementation. Main findings: Health interventions for tuberculosis contact investigation by community health workers in three global lists of high-burden countries for tuberculosis, human immunodeficiency virus-associated tuberculosis, and multidrug-resistant/rifampicin-resistant tuberculosis are generally acceptable and feasible, but their effectiveness depends heavily on a clear implementation strategy and the ability to address multilevel barriers at the individual, technology, and service system levels.Added knowledge: Given the ongoing development of digital health interventions for TB contact investigation by community health workers, this review provides a novel contribution by systematically mapping seven dimensions of implementation strategies, classifying barriers and facilitators at multiple levels, and highlighting the importance of synergies between human actors and digital systems in determining implementation success.Global health impact for policy and action: These findings confirm that strategic integration of mHealth into national tuberculosis policies and programs – with sustained funding, community health worker capacity building, digital system interoperability, and implementation-based monitoring – is crucial for closing the contact investigation gap and accelerating the achievement of the global tuberculosis elimination target.
11 June 2026
Read appraisal →Gene
Based on WGCNA and machine learning studies, SMURF2 drives NSCLC malignant transformation, ferroptosis, and macrophage polarization by ubiquitinating SPP1
BACKGROUND: Non-small cell lung cancer (NSCLC) is a common type of lung cancer with poor prognosis and high mortality in advanced stages. Although secreted phosphoprotein 1 (SPP1) is associated with the progression of NSCLC, its specific mechanisms remain to be explored. METHODS: Through integrated bioinformatics analysis combining weighted gene co-expression network analysis (WGCNA), differential expression analysis, gene enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO), and machine learning techniques incorporating database predictions, core genes were identified. Experimental validation was conducted using Western blot, co-immunoprecipitation (Co-IP), cycloheximide (CHX) chase assay, Ubiquitin Immunoprecipitation (Ub-IP), colony formation, flow cytometry, transwell assay, reactive oxygen species (ROS) detection, and quantitative real-time PCR (qRT-PCR). The functional impact of SPP1 on tumor growth was further confirmed through mouse models and immunohistochemistry (IHC). RESULTS: Based on WGCNA and machine learning, SPP1 was identified as a core gene. SMURF2 ubiquitinated and degraded SPP1. Rescue experiments confirmed that SMURF2 inhibited proliferation and invasion, and promoted apoptosis in NSCLC cells, which were reversed by SPP1 overexpression. Furthermore, SMURF2 enhanced ferroptosis and modulated macrophage polarization via the same ubiquitination mechanism. Animal studies verified that SMURF2-mediated ubiquitination and degradation of SPP1 suppressed tumor growth. CONCLUSION: This study aims to investigate the role of SMURF2-mediated ubiquitination of SPP1 in inhibiting malignant progression, promoting ferroptosis, and modulating macrophage polarization in NSCLC, thereby providing a theoretical foundation and new insights for understanding and targeting NSCLC.
10 June 2026
Read appraisal →Clinical oral investigations
Changes in the sagittal pharyngeal airway dimension following levelling and alignment in class II division 2 patients: a prospective clinical trial
OBJECTIVES: This study aimed to evaluate changes in the sagittal pharyngeal airway dimensions (SPAD) and the related mandibular position following maxillary levelling and alignment in late adolescent Class II division 2 (Class II/2) patients. MATERIALS AND METHODS: Thirty Class II/2 patients (mean age 14.07 ± 1.21 years) were included. Pre-treatment orthodontic records were acquired, including panoramic and lateral cephalometric x-rays (T0), photographs, and study models, followed by the onset of levelling and alignment in the upper arch. Lateral cephalometric x-rays and photographs were repeated six months later at the termination of this stage (T1), and measurements were performed in comparison to T0. SPAD was assessed at the nasopharyngeal, oropharyngeal, and laryngopharyngeal levels. Mandibular position was evaluated using the Ba-Ar distance, while sagittal and vertical skeletal relationships were assessed using SNA°, SNB°, ANB°, and FMA°. Upper incisor inclination was accounted for using the U1-FH° measurement. RESULTS: Alignment of the maxillary arch in Class II/2 patients elicited a significant SPAD increase at the nasopharyngeal and the oropharyngeal levels (p < 0.001), with a non-significant change evoked at the laryngopharyngeal level (p = 0.42). Antero-posteriorly, a significant increase in the SNAº, SNBº, together with a significant decrease in the ANBº angle have been documented at T1. A significant increase in the Ba-Ar has been noted at T1 in contrast to T0, in addition to a significant reduction in the vertical dimension (FMAº). Dentally, a significant increase in the U1-FHº has been documented following the implemented treatment stage (p < 0.001). CONCLUSIONS: Results of this study may suggest that maxillary levelling and alignment in Class II/2 patients during the decelerating growth phase is associated with augmentation of the nasopharyngeal and oropharyngeal airways. These changes coincide with the significant labial tipping of the maxillary incisors and subsequent functional mandibular advancement, which collectively improve the intermaxillary relationship and reduce the vertical skeletal dimension. While natural growth cannot be entirely excluded, the rapid change suggests that these improvements are closely linked to the functional mandibular response. CLINICAL RELEVANCE: Maxillary alignment in Class II division 2 cases may enhance upper airway patency by triggering a forward functional mandibular response, highlighting a potential link between orthodontic correction and upper airway patency. Name of the Registry: ClinicalTrials.gov Trial Registration Number: NCT06602518 Date of Registration: 19/09/2024 "Retrospectively registered". URL: https://clinicaltrials.gov/study/NCT06602518?cond=nct06602518&rank=1.
7 June 2026
Read appraisal →Environmental monitoring and assessment
YOLO-geochemical integration for real-time aeolian dust monitoring: a critical review of signatures, health risks, and policy implications
Aeolian dust is one of the most pervasive natural air pollutants, strongly influencing atmospheric chemistry, visibility, climate feedback, and human health. This review critically evaluates the integration of geochemical fingerprinting techniques and YOLO-based deep learning frameworks for real-time aeolian dust monitoring, with the overarching objective of advancing source attribution, health risk assessment, and evidence-based policy formulation in dust-prone regions. The Middle East, particularly the Qatar Peninsula, experiences recurrent dust storms that transport mineral particles and trace metals, contributing to degraded air quality, reduced solar energy efficiency, and elevated risks of respiratory and cardiovascular diseases. Traditional monitoring approaches including satellite observations, ground-based measurements, and geochemical analyses such as inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) have improved source apportionment and compositional understanding, yet they often lack real-time detection and high-resolution spatiotemporal coverage. Emerging artificial intelligence (AI) tools, particularly the You Only Look Once (YOLO) deep learning framework, offer transformative opportunities for atmospheric dust monitoring by enabling rapid, automated plume detection across multispectral and hyperspectral datasets. This review demonstrates that ICP-MS, SEM-EDS, and XRD, when applied in combination, provide robust multi-dimensional source fingerprinting: ICP-MS isotopic ratios discriminate crystal from anthropogenic contributions, SEM-EDS resolves particle-level morphology and elemental composition, and XRD quantifies mineralogical assemblages that vary systematically with source region and transport pathway. Concurrently, successive YOLO generations (YOLOv5 through YOLOv10) have achieved real-time dust plume detection with mAP values exceeding 88% on satellite and UAV imagery. The proposed multimodal YOLO-geochemical integration framework, in which YOLO-guided plume detection triggers targeted field sampling and geochemical labels iteratively retrain detection models, enables compositionally resolved, real-time exposure mapping that directly supports inhalation risk assessment against WHO PM2.5 and PM10 thresholds and metal-specific reference concentrations. Collectively, these findings establish a scalable, evidence-based monitoring architecture that strengthens early-warning systems, informs air quality regulation, and advances global strategies to mitigate the environmental and public health burdens of dust storms.
7 June 2026
Read appraisal →BMJ open
Can digital self-screening improve identification of chronic dyspnoea in Australian general practice? A proof-of-concept protocol for the BREATHE SMART trial.
INTRODUCTION: Chronic dyspnoea is a prevalent and clinically significant symptom, often indicative of underlying cardiorespiratory disease. It is frequently under-reported by patients and under-recognised in primary care, with these challenges exacerbated in rural and remote communities where disease burden is greater and patients experience barriers to timely diagnosis and management. The BREATHE SMART trial aims to implement and evaluate an innovative, fully digital self-screening system for chronic dyspnoea, integrated into general practice workflows and information technology infrastructure. This approach seeks to enhance early detection and management of chronic cardiorespiratory conditions across diverse practice settings. METHODS AND ANALYSIS: This multisite proof-of-concept study will test a software platform delivering a preconsultation self-screening questionnaire across 40 general practices in urban, rural and remote Australia. The system identifies eligible patients (≥18 years, consenting to SMS communication with their practice), issues an automated SMS that administers a validated dyspnoea screening questionnaire, and summarises responses for integration into the electronic medical record. Process evaluation will assess acceptability and utility using deidentified audit data, software metrics and qualitative feedback from patients, staff and general practitioners (GPs) via surveys, interviews and focus groups. Approximately 12 000 patients will be screened over 12 months. Primary outcomes will include the proportion completing self-screening and prevalence of chronic dyspnoea and secondary outcomes will include the rate of newly diagnosed chronic dyspnoea-related conditions (ie, asthma, chronic obstructive pulmonary disease and heart failure) in the preceding 12 months and during the intervention period. ETHICS AND DISSEMINATION: Ethics approval was granted by the University of New South Wales Human Research Ethics Committee (HREC) (iRECS6645) and the University of Notre Dame Australia HREC (2024-155). Participating practices and each GP will provide written, informed consent. All patients being screened will provide electronic informed consent. Results of the study will be disseminated through various forums, including peer-reviewed publications and presentation at national and international conferences. Following the study, participating practices will be provided with a summary of the findings of the study, together with a full copy of any publications and a plain language statement for participants, which will be made available in the practices. TRIAL REGISTRATION NUMBER: ACTRN12624001451594.
1 June 2026
Read appraisal →PLoS medicine
Characterization of the VHH-Fc construct rimteravimab in healthy adults and patients hospitalized for mild-to-moderate COVID-19: Two Phase 1 randomized clinical trials
BACKGROUND: Variable Heavy domain of Heavy chains (VHH) are innovative tools to target unique epitopes, yet few have been developed as heavy chain-only antibodies for clinical use. Rimteravimab (referred to here as XVR011) is a humanized antibody developed for the treatment of mild-to-moderate coronavirus disease 2019 (COVID-19), consisting of two identical VHHs targeting the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, with a human immunoglobulin (Ig) G1 fragment constant of antibody (Fc), silenced for Fc effector functions. We conducted two Phase 1 studies in healthy volunteers or hospitalized COVID-19 patients to evaluate its safety, tolerability, pharmacokinetics and immunogenicity. METHODS AND FINDINGS: A randomized, double-blinded, single-center, placebo-controlled, single ascending dose study was performed in healthy volunteers (Phase 1a, EXEVIR0102, EudraCT 2021-003707-17), in parallel to an open-label, multi-center, single ascending dose study in patients hospitalized for mild to moderate COVID-19 (Phase 1b, EXEVIR0101, EudraCT 2020-005299-36, NCT04884295). Participants received a single intravenous infusion of 250, 500 or 1,000 mg of XVR011. The primary objective for both trials was the safety and tolerability of XVR011. Pharmacokinetics were evaluated as a secondary objective in Phase 1a and as an exploratory objective in Phase 1b. Efficacy (evaluated as respiratory parameters and COVID-19 clinical status) and antiviral activity in patients were evaluated as a secondary objective in Phase 1b. Immunogenicity was evaluated as an exploratory objective. Part 2 of the EXEVIR0101 study (initially a phase 1b/2 study) was not conducted due to the loss of XVR011 potency against SARS-CoV-2 Omicron BA.2. Demographics, safety, efficacy, and immunogenicity were analyzed using descriptive statistics, while pharmacokinetics were analyzed with noncompartmental pharmacokinetics (PK) modeling. In the Phase 1a study, there were no infusion-related reactions, serious treatment-emergent adverse events (TEAEs) or TEAEs grade ≥3. 22/30 volunteers (73.3%) reported 53 TEAEs (49 Grade 1, 4 Grade 2) with none being related to XVR011. The most common TEAE was headache (n = 8, 26.7%) in various treatment groups. In the Phase 1b study, 27 hospitalized patients were enrolled, and followed up to 30 days. Seven patients (25.9%) reported a total of 15 TEAEs, the majority (80%) being mild to moderate (Grade 1-2). There were no treatment-related serious TEAEs. All TEAEs resolved by the end of the study. Peak exposure (maximal concentration, Cmax) and systemic exposure (area under the curve, AUC0-t, and AUC0-inf) for XVR011 increased dose-proportionally. Geomean half-life ranged from 15.4 to 17.0 days in Phase 1a, while individual half-life ranged from 11.4 to 15.6 days in Phase 1b. SARS-CoV-2 viral load, as detected in nasopharyngeal samples by reverse transcription and quantitative polymerase chain reaction (RT-qPCR), decreased similarly in all cohorts compared to baseline. No treatment-induced anti-drug antibodies (ADA) were detected in Phase 1a. In Phase 1b, higher XVR011 concentrations increased the likelihood of ADA formation, without impacting pharmacokinetics and pharmacodynamics. No obvious dose-response in COVID-19 clinical status or respiratory parameters was observed. Technological limitations included study size, absence of placebo for the Phase 1b, absence of repeated dosing, evolving SARS-CoV-2 variants and standard-of-care. CONCLUSIONS: XVR011 displayed a favourable safety, tolerability, pharmacokinetics, and immunogenicity profile, both in healthy volunteers and in patients hospitalized for mild to moderate COVID-19. These data pave the way for the design and clinical development of VHH-Fc constructs.
31 May 2026
Read appraisal →European journal of pediatrics
Effect of probiotics on allergic rhinitis in children; A systematic review and meta-analysis
BACKGROUND: Allergic rhinitis (AR) is a prevalent chronic inflammatory disorder in children, often undiagnosed and associated with reduced quality of life and risk of comorbidities such as asthma. Probiotics have been found to be potential modulators of immune responses in allergic conditions. This systematic review and meta-analysis evaluated the efficacy of probiotics compared with placebo in the management of pediatric AR. METHODS: Searches were conducted in accordance with a pre-registered protocol using MEDLINE (PubMed), the Web of Science, Scopus, and Embase through April 8, 2025. Randomized controlled trials (RCTs) of children aged < 18 years that compared probiotics, prebiotics, or synbiotics with a placebo were considered for inclusion. Outcomes of interest included clinical and laboratory parameters. Random-effects models with Hedges' g were used; heterogeneity was assessed via I2. RESULTS: Fourteen RCTs involving 1,739 children were included. Meta-analysis revealed a significant reduction in nasal symptom scores (SMD = -1.40; 95% CI: -2.26 to -0.54; I2 = 91.3%) and eye symptom scores (SMD = -3.59; 95% CI: -5.84 to -1.33; I2 = 94.3%). Quality of life (RQLQ) was also significantly improved (SMD = -2.98; 95% CI: -4.85 to -1.12; I2 = 95.7%). Among laboratory markers, a small but significant increase in serum eosinophils was observed (SMD = 0.29; 95% CI: 0.06 to 0.52; I2 = 0%). CONCLUSION: Probiotics were associated with improvements in nasal and ocular symptoms and quality of life in children with AR; however, these findings should be interpreted with caution due to substantial heterogeneity across studies. Probiotics may have a potential role as an adjunctive therapy, but further high-quality, standardized trials are needed. PROSPERO registration code: CRD420250654461 on 24 February 2025. What is Known • Probiotics have been investigated as adjunctive therapy for pediatric allergic rhinitis because of their potential immunomodulatory effects. • Previous studies and meta-analyses have reported inconsistent findings regarding the clinical and immunological effects of probiotics in allergic rhinitis. What is New •This meta-analysis demonstrated significant improvements in nasal symptoms, ocular symptoms, and quality of life in children with allergic rhinitis receiving probiotics versus placebo. • Probiotic supplementation showed no consistent beneficial effects on immunological markers, while substantial heterogeneity remained across studies.
30 May 2026
Read appraisal →Journal of breath research
Investigating short-term lung inflammation using exhaled breath VOCs from exposure to candles: a randomized controlled crossover study among young mild asthmatics
Lung inflammation is associated with a response to pollution or irritants and is reported to release volatile organic compounds (VOCs) in breath, that can be monitored non-invasively. An approach to studying short-term lung inflammation is exposure to environmental emissions. This study used brief exposure to PM₂.₅ emissions from two types of burning candles to evaluate potential differences in airway inflammation among mildly asthmatic individuals. The aim was to explore VOCs linked to lung inflammation by exposure to candle emissions; emissions from candle A, a newly developed candle with a smaller emission to the surroundings and candle B, a well-characterised commercially available candle. Seventeen non-smoking asthmatics (11 female) with a mean age of 21.9 years participated in a randomised controlled double-blind crossover study including two exposure sessions: i) air with candle A emissions (mean PM2.554.1µg m-3), and ii) air with candle B emissions (mean PM2.598.4µg m-3). Participants underwent two five-hour, double-blind exposure sessions in a controlled climate chamber, each involving emissions from one of two candle types. Exhaled breath was collected at baseline (0 h), immediately post-exposure (5 h), and the following morning (24 h). VOCs were analysed using both a targeted panel of 23 predefined compounds and an untargeted workflow, which yielded 21 breath-derived VOCs after ambient-blank filtering. Statistical comparisons used Wilcoxon signed-rank tests with Benjamini-Hochberg correction. Across both exposure sessions, ten exhaled VOCs showed significant changes between baseline and immediate post-exposure (5 h), including increases in several aldehydes and decreases in selected sulfur-containing compounds. These changes were not present at 24 h, where samples resembled baseline profiles. The ten VOCs were identified from a combined dataset of 23 targeted compounds and 21 untargeted breath-derived VOCs; only the untargeted analysis yielded significant findings after correction. PCA demonstrated clear separation between baseline and 5 h samples, particularly for candle B, for which 0 h, 5 h and 24 h samples formed distinct clusters. This candle-specific response produced a multi-VOC signature capable of distinguishing individuals immediately after exposure, suggesting that exhaled VOCs can reflect short-term airway inflammatory responses. The findings suggest that an emission-specific, exhaled breath, multi-VOC profile can detect individuals exposed to inflammation triggers, such as candles. This indicates that VOCs could serve as biomarkers for detecting short-term airway inflammation, as evidenced by the systematic difference in VOCs observed 5 h after exposure to candle B. This research contributes to the advancing field of VOC-based health monitoring and might affect public health implications.
30 May 2026
Read appraisal →Biomedical physics & engineering express
Biwt-UNet: lung nodule segmentation via wavelet transform and multi-scale feature fusion
Lung nodule segmentation is a crucial step in computer-aided diagnosis of lung diseases and is of great significance for early detection and diagnosis of lung cancer. Traditional segmentation methods face many challenges, such as blurred nodule boundaries that are difficult to distinguish effectively, and the diversity of nodule sizes and shapes, which together increases the difficulty of segmenting lung nodules. Existing approaches often fail to address these issues adequately, highlighting the need for more effective solutions. To meet these challenges, this paper proposes a hybrid lung nodule segmentation approach-Biwt-UNet that integrates improved Haar wavelet transform and multi-scale feature information from the multi-scale fusion module and bi-encoder fusion module proposed in this study. The approach enables accurate segmentation of lung nodules of varying sizes, diverse shapes and, in particular, indistinct boundaries. Experimental results on the public LIDC-IDRI dataset demonstrate that Biwt-UNet achieves excellent segmentation performance. It significantly surpasses other state-of-the-art methods with an average dice similarity coefficient of 90.18%, an average intersection over union of 82.67%, and an average normalized surface dice of 96.39%, fully validating the effectiveness and accuracy of the proposed model in CT-based lung nodule analysis. Moreover, ablation studies further confirm the individual contributions of each architectural component and show that the best performance is achieved within an acceptable parameter budget. This study provides a new technical perspective for automatic lung nodule segmentation and is expected to assist physicians in more efficient diagnosis.
29 May 2026
Read appraisal →BMJ open
Self-management experiences and needs in patients with chronic obstructive pulmonary disease: a meta-synthesis of qualitative studies
OBJECTIVES: To systematically review qualitative studies on the self-management experiences and needs of patients with chronic obstructive pulmonary disease (COPD) and to provide evidence for developing needs-based self-management intervention strategies. DESIGN: A systematic review and meta-aggregation of qualitative studies conducted in accordance with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research statement. DATA SOURCES: PubMed, Cochrane Library, Web of Science, Scopus, CINAHL, Embase, CNKI, Wanfang Database, VIP Database and CBM were searched from database inception to March 2025 for qualitative studies on COPD self-management experiences and needs. ELIGIBILITY CRITERIA: Qualitative studies involving adults (≥18 years) with a confirmed diagnosis of COPD that reported patients' real feelings, experiences and needs during the self-management process in any setting (hospital, community or home). Studies were excluded if the full text could not be obtained or data were incomplete, if they were duplicate publications or if they were not published in Chinese or English. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened titles, abstracts and full texts, extracted data using a standardised form and assessed methodological quality using the 2016 Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. Findings (participants' quotations and authors' interpretive statements) were synthesised using a JBI meta-aggregation approach to develop categories and overarching synthesised findings. RESULTS: 15 qualitative studies were included, comprising 251 patients with COPD. A total of 52 findings were identified, grouped into 13 categories and synthesised into 4 overarching findings: (1) physical symptom burden strongly shapes psychological well-being during COPD self-management; (2) patients face multiple and interrelated barriers to effective self-management; (3) some patients develop active coping strategies and self-management resources; and (4) patients express multidimensional and continuing support needs. CONCLUSIONS: Patients with COPD experience substantial physical and psychological distress and face multiple challenges in the self-management process. Healthcare professionals should prioritise patients' lived experiences, establish comprehensive and coordinated support systems and develop diversified, needs-based intervention strategies to address patients' multidimensional needs, thereby enhancing self-management capacity and improving quality of life. PROSPERO REGISTRATION NUMBER: CRD42024607051.
29 May 2026
Read appraisal →PLoS medicine
Sequential chemo-immunotherapy followed by standard versus reduced thoracic radiotherapy for older and/or frail stage III non-small-cell lung cancer: A randomized open-label cohort trial
BACKGROUND: The appropriateness of concurrent chemoradiotherapy (cCRT) for older or clinically vulnerable stage III unresectable non-small-cell lung cancer (NSCLC) patients remains contentious. Furthermore, the survival implications of de-escalating thoracic radiotherapy (RT) intensity in this population have not been conclusively elucidated. METHODS AND FINDINGS: We conducted a phase II randomized, open-label, two-cohort (non-comparative) trial at a tertiary hospital in China (NCT05557552). Between September 30, 2022 and April 30, 2024, we enrolled 56 older and/or frail patients with stage III NSCLC who were ineligible for cCRT. The primary endpoint was the 1-year progression-free survival (PFS) rate estimated using the Kaplan-Meier method. Secondary endpoints included objective response rate (ORR), overall survival (OS), and safety. In the intention-to-treat (ITT) set, which included all 56 randomized patients who received at least one dose of study treatment, the 1-year PFS was 84.3% (95% confidence interval [CI] [70.3%, 98.3%]) in the standard RT group and 70.7% (95% CI [54.3%, 87.1%]) in the reduced RT group. In the per-protocol set (53 patients), the 1-year PFS was 82.9% (95% CI [68.9%, 98.8%]) in the standard RT group and 73.4% (95% CI [58.3%, 92.4%]), with a median follow-up of 24 months. Among 56 patients in the safety analysis set, 71.4% of patients experienced grade 3/4 adverse events (AEs) in the standard RT group and 53.6% in the reduced RT group. One patient (3.6%) in the reduced RT and three patients (10.7%) in the standardized RT experienced grade 5 AEs. The main limitations are the non-comparative design, small sample size, and lack of power to establish non-inferiority or superiority. CONCLUSION: The current study suggested that reduced RT combined with sequential chemo-immunotherapy might be feasible for older/frail patients intolerant to cCRT, showing numerically similar survival outcomes. These exploratory findings warrant confirmation in larger, adequately powered randomized trials. TRIAL REGISTRATION: The trial had been registered on ClinicalTrials.gov on Sep 30, 2022. ClinicalTrials.gov NCT05557552.
29 May 2026
Read appraisal →BMJ open
Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol
INTRODUCTION: Chronic obstructive pulmonary disease (COPD) has an unpredictable clinical course, causing difficulties in short-term mortality prediction, overtreatment and delayed palliative care. Existing prediction models are limited and lack applicability to Chinese elderly patients with advanced COPD. Given the heavy disease burden and limited palliative care in China, we designed this multicentre cohort study to develop a 6-month mortality prediction model for elderly patients with advanced COPD to aid risk stratification, timely palliative care and efficient healthcare resource allocation. METHODS AND ANALYSIS: Patient recruitment has been ongoing since May 2024 and will be completed by December 2026, with a 12-month follow-up to be completed by December 2027. Eligible patients are being enrolled, and multidimensional baseline data including demographic characteristics, clinical indicators, laboratory results, comprehensive geriatric assessment and COPD-specific prognostic factors are being systematically collected. All participants will receive 12 months of standardised follow-up (monthly for the first 6 months and quarterly thereafter) to monitor 6-month all-cause mortality (primary outcome), as well as survival duration, end-of-life healthcare utilisation and do-not-resuscitate status (secondary outcomes). After completion of data collection, we will employ multiple machine learning algorithms to develop and internally validate a 6-month mortality prediction model with pre-specified centres reserved for external validation. Model performance will be evaluated by discrimination and calibration and head-to-head comparisons with the Body Mass Index, Airflow Obstruction, Dyspnoea and Exercise Capacity (BODE) and Age, Dyspnoea and Airflow Obstruction (ADO) indices will be conducted to verify its clinical value. The findings will provide a China-specific prediction tool for elderly patients with advanced COPD to guide clinical intervention, palliative care referral and healthcare resource allocation. ETHICS AND DISSEMINATION: This study was approved by the Biomedical Ethics Review Committee of West China Hospital, Sichuan University (No. 2024-2662) and registered at ChiCTR2500100351. Informed consent is being obtained from all participants. Results will be published in peer-reviewed journals and presented at academic conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500100351.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Implementation of long-term noninvasive positive airway pressure therapy in patients with COPD: a systematic review and meta-analysis of acceptance and adherence
BACKGROUND: Long-term noninvasive positive airway pressure (PAP) therapy is an effective, albeit complex, intervention for obstructive sleep apnoea (OSA) and/or chronic hypercapnic respiratory failure (CHRF) in patients with chronic obstructive pulmonary disease (COPD). PAP uptake and use is often reported in studies designed for other purposes. Systematic review methods were used to summarise acceptance of and adherence with PAP therapy. METHODS: A systematic search for studies that reported the proportion of patients that either declined or accepted PAP therapy and/or objective use of newly prescribed PAP therapy in patients with COPD was conducted according to a predefined protocol (PROSPERO CRD42021259262). Meta-analysis and meta-regression techniques were used to establish summary effect estimates and explore heterogeneity. RESULTS: On average, PAP therapy was declined or discontinued by 14% (95% CI 10-19) of participants, often within 6 weeks of initiation among studies reporting repeated measures. The pooled median adherence was 6.3 h·day-1 (95% CI 5.9-6.6). While meta-regression found higher acceptance (p=0.03) and shorter use per day (p<0.01) for the indication of OSA versus CHRF, this explained only some heterogeneity in the summary effect estimates. Summary of study-level variables associated with acceptance or adherence highlighted limitations in the available literature. CONCLUSIONS: An anticipated six out of seven patients with COPD newly prescribed PAP therapy will be successful using therapy; however, heterogeneity was substantial among studies, and only partly explained by indication for use. Prospective studies to explore barriers to use and detailed reporting of acceptance and adherence in studies on PAP therapy in patients with COPD are needed.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Nutrition and growth of primary ciliary dyskinesia patients: a systematic review.
BACKGROUND: Growth, nutritional status and intake can be crucial for the management and prognosis of chronic lung diseases, including primary ciliary dyskinesia (PCD); yet research in PCD is scarce. In this study, we systematically reviewed the existing literature and summarised the evidence on growth, nutritional status and intake of patients with PCD. METHODS: We conducted searches in Medline, Embase, Scopus and PubMed limited to the past 20 years, due to changes in the diagnosis of PCD. Eligible studies included cross-sectional, case-control and prospective studies, and case series with >5 people that reported on the growth, nutritional status or intake of patients with PCD. We performed additional searches through reference list checking and in medRxiv for relevant preprints. The review was registered in PROSPERO (CRD420251065699). RESULTS: 50 studies were included in the qualitative synthesis. We observed variability in growth-related outcomes, with some studies reporting reduced z-score for height, body weight or BMI, while others reported no growth differences in patients with PCD. The prevalence of undernutrition ranged from 4 to 11%; underweight ranged from 0 to 50%. Overweight and obesity estimates ranged from 3 to 25%. The prevalence of vitamin D insufficiency was 54%, while deficiency was reported in 18-26% of study participants. Only three studies reported on the nutritional intake of patients with PCD. DISCUSSION: We observed considerable variability in growth-related outcomes and a lack of nutritional intake-related data in patients with PCD. The findings we noted related to malnutrition and vitamin D insufficiency/deficiency are alarming and warrant further investigation.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Muscle sympathetic nerve activity in COPD: a systematic review and meta-analysis
BACKGROUND: Cardiovascular disease is a primary driver of mortality in COPD. Elevated sympathetic nerve activity is a key proposed mechanism, but the magnitude of this autonomic overactivity has not been quantified in a pooled analysis. OBJECTIVE: To determine if adults with COPD exhibit higher resting muscle sympathetic nerve activity (MSNA) than healthy controls, assess associated cardiorespiratory effects, and evaluate the short-term impact of COPD interventions on MSNA. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, eight databases were searched from inception to 15 May 2025 to inform this systematic review and meta-analysis (PROSPERO CRD420251044931). Eligible studies included adults with spirometry-defined COPD in whom MSNA was measured by microneurography at rest. Primary outcomes were MSNA burst frequency (bursts·min-1) and incidence (bursts per 100 heartbeats). Secondary outcomes included resting heart rate and blood pressure. RESULTS: A total of 11 studies (171 COPD participants, 105 controls) met inclusion criteria. Compared with controls, COPD was associated with markedly higher MSNA, burst frequency (+18.5, 95% CI 9.4-27.7 bursts·min-1) and burst incidence (+21.3, 95% CI 9.2-33.4 bursts per 100 heartbeats). Resting heart rate was also elevated (+10.7, 95% CI 6.1-15.3 beats·min-1), while blood pressure did not differ significantly. Three studies on noninvasive ventilation or inhaled β2-agonists found no significant pooled changes in MSNA. CONCLUSIONS: Resting MSNA is significantly elevated in patients with COPD despite normal blood pressure. This chronic sympathetic excitation likely contributes to COPD-related cardiovascular morbidity and exercise intolerance, highlighting the importance of developing therapies that reduce sympathetic nerve activity.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Nonpharmacological dyspnoea-relief strategies in lung cancer: a systematic review and network meta-analysis
BACKGROUND: Dyspnoea is a common and distressing symptom in lung cancer patients. While various dyspnoea-relief strategies show promise, their comparative effectiveness remains unclear. METHODS: We conducted a systematic review and network meta-analysis of randomised controlled trials on nonpharmacological dyspnoea-relief strategies in lung cancer patients, using studies published until 12 June 2025 from the databases PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform. The primary outcome was dyspnoea score post-intervention. RESULTS: 4138 participants from 46 randomised controlled trials were included in the analysis. Among 10 nonpharmacological dyspnoea-relief strategies in lung cancer patients, acupressure (surface under the cumulative ranking curve (SUCRA) 98.7%, Hedges' g -1.96, 95% CI -2.92 to -0.99) demonstrated the highest efficacy, followed by fan (SUCRA 83.7%, Hedges' g -1.11, 95% CI -1.72 to -0.50), and multicomponent interventions (SUCRA 80.7%, Hedges' g -1.01, 95% CI -1.49 to -0.52). In post-operative lung cancer patients, multicomponent interventions (SUCRA 97.4%, Hedges' g -1.55, 95% CI -2.16 to -0.94) were the most effective, followed by cognitive behavioural therapy (SUCRA 74.8%, Hedges' g -0.90, 95% CI -1.75 to -0.05). For patients with pre-existing dyspnoea, acupressure (SUCRA 90.9%, Hedges' g -1.72, 95% CI -3.01 to -0.42) and fan therapy (SUCRA 77.3%, Hedges' g -1.18, 95% CI -2.10 to -0.27) were the top interventions. CONCLUSIONS: Acupressure is the primary strategy for alleviating dyspnoea in lung cancer patients, including those with pre-existing dyspnoea, followed by fan therapy, while multicomponent interventions are recommended for post-operative patients.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Validating exacerbations of asthma in electronic health records: a systematic review
BACKGROUND: Previous studies have shown that the algorithms and code lists used to define asthma exacerbations vary across different sources of data, if reported at all. Defining and validating asthma exacerbations in electronic health records (EHR) would help to improve future research on asthma using EHR by leading to more consistent and comparable evidence. METHODS: We systematically reviewed the literature to evaluate studies that define exacerbations of asthma in EHR and report which algorithms have the highest validity. An adapted version of the QUADAS-2 designed for this review was used to assess risk of bias. RESULTS: Of the studies yielded by the search, only five met the inclusion criteria. Eligible studies used algorithms that contained codes from versions or modifications of either the 9th or 10th revisions of the International Statistical Classification of Diseases and Related Health (ICD-9 or ICD-10), and validity scores varied. Using the ICD-9 code 493 within algorithms to detect asthma exacerbations, sensitivity scores varied from 44.8% to 91.28% and specificity was >85%. Using the ICD-9 code 493.xx as the principal and secondary diagnosis in claims data, validity measures were all >85%. Using the ICD-10 code J45, scores for sensitivity, specificity and negative predictive value were also all >85%. CONCLUSIONS: Algorithms have been used to identify asthma exacerbations in EHR with varying degrees of validity. Algorithms including the ICD-9 code 493.xx or the ICD-10 code J45 to detect asthma exacerbations had high validity scores. However, there was a risk of bias in these studies and urgent work is needed using robust methods to validate definitions for future research using EHR.
29 May 2026
Read appraisal →BMJ open
Does a protocolised oxygenation target impact clinical outcomes in mechanically ventilated patients in the intensive care unit across the 10 academic and community hospitals in the UCHealth system? Protocol and statistical analysis plan for an EHR-embedded pragmatic stepped-wedge randomised controlled trial.
INTRODUCTION: We designed the Optimising Care in Critically Ill at the UCHealth by Liberalising the Target O₂ in Mechanically ventilated intensive care unit (ICU) Patients to determine the effectiveness of a multimodal educational and electronic health record order panel intervention in limiting occult hypoxaemia and hyperoxaemia in patients receiving invasive mechanical ventilation by targeting a prespecified oxygen saturation (SpO₂ 90%-96%) range. METHODS AND ANALYSIS: This trial is a pragmatic electronic health record-embedded, multisite, cluster-randomised, stepped-wedge implementation of a multimodal educational and electronic health record order panel intervention aimed at achieving a standardised intermediate target range of SpO₂ (90%-96%) or partial pressure of oxygen (PaO₂ 60-100 mm Hg) in mechanically ventilated adult patients admitted to the ICU across a 10 hospital health system that includes a large academic centre and smaller community hospitals.The primary endpoint is ventilator-free days to day 30, defined as the number of days alive and not receiving support through invasive mechanical ventilation following the first initiation of invasive mechanical ventilation in a participating ICU during the hospitalisation. Secondary outcomes include a variety of clinical endpoints: hospital-free days to day 30, all-cause mortality to day 90, need for supplemental oxygen at discharge and incidence of occult hypoxaemia and hypoxaemia. We will analyse primary and secondary endpoints using a mixed effects modelling framework, with specific distributions chosen depending on the type of outcome (eg, binary, count, ordinal, time-to-event). ETHICS AND DISSEMINATION: Research is performed under a waiver of informed consent for minimal-risk research, as approved by Colorado Multiple Institutional Review Board (24-0065). Results will be disseminated in peer-reviewed publications and at national and international conferences. TRIAL REGISTRATION NUMBER: NCT06501118.
28 May 2026
Read appraisal →European radiology experimental
Who labels best? Radiologists, rules, or large language models for CT reports on pulmonary embolism.
OBJECTIVE: To compare open-weight and proprietary large language models (LLMs), a rule-based extractor (RBE) and radiologists for labelling pulmonary embolism CT reports, and to test whether a hybrid RBE-LLM workflow improves labelling performance. MATERIALS AND METHODS: This single-centre retrospective study included structured CT reports from October 2021 to March 2025. Three labelling pipelines were evaluated: an RBE; a model-agnostic LLM extractor (18 open-weight, four GPT-4 variants); and a hybrid pipeline routing only RBE failures to an LLM. Ground truth was defined at the report-text level by deterministic schema matching for initially RBE-valid fields and blinded adjudication of RBE-invalid fields by two attending radiologists. Eight radiologists provided a human baseline. Outcomes included F1 scores, accuracy, LLM-based salvage of RBE failures, and labelling time. RESULTS: In total, 2,923 reports from 2,923 patients (mean age 66 ± 17 years; 1,465 women) were included. Falcon3-10b and GPT-4.1-mini achieved similar item-level performance (F1 0.98 [95% CI, 0.97-0.98] for both; p = 0.70) and both exceeded the RBE (F1 0.81 [95% CI, 0.80-0.82]; p < 0.001). Salvage of RBE failures was comparable between open-weight and proprietary models (88.1% vs 91.9%; p = 0.12). The hybrid RBE-LLM workflow achieved 99.8% accuracy and F1 0.99 (0.98-0.99), exceeding both the RBE and pooled radiologists (F1 0.92 [95% CI, 0.90-0.93]; all p < 0.001). CONCLUSION: Schema-constrained open-weight and proprietary LLMs exceeded rule-based extraction and, at the upper end of performance, matched a pooled radiologist label-transfer baseline. A rules-first, targeted LLM workflow enabled near-perfect extraction from finalised structured pulmonary embolism CT reports. RELEVANCE STATEMENT: A rules-first LLM workflow can automate high-fidelity extraction of structured CT findings from finalised radiology reports, enabling scalable, auditable, and more consistent cohort curation for clinical research, registries, and quality improvement. KEY POINTS: A hybrid rules-first workflow combining a rule-based extractor (RBE) with targeted large language model (LLM) salvage achieved the highest overall performance for labelling of pulmonary embolism CT reports (F1, 0.99; accuracy, 99.8%). The top standalone open-weight and proprietary LLMs (Falcon3-10b and GPT-4.1-mini) both exceeded the RBE and, at the upper end of performance, matched a pooled radiologist label-transfer baseline. The hybrid workflow reduced cohort-curation time from 32.2 h for radiologists to 1.0 h while reducing LLM calls by 85.6%, because the LLM was only triggered for rule-failed fields.
28 May 2026
Read appraisal →mSphere
Novel machine learning-based approach to identify viral biomarkers of human respiratory emissions from oral and nasal metagenomes
Humans spend approximately 90% of their lives in built environments, making virus transmission indoors a key determinant of health. Environmental sampling of respiratory viral pathogens is often challenging because of frequent non-detect measurements. Non-detect measurements do not differentiate between samples containing low or no pathogens from samples that simply lack respiratory expulsions altogether. This ambiguity can be resolved by scanning samples for a biomarker of human respiratory emissions. To do so, reliable biomarkers for environmental monitoring need to be identified. Ideal biomarkers are prevalent across individuals, abundant, and unique to the human respiratory tract. Here, we present a new machine learning-based approach to query for suitable biomarker candidates from publicly available metagenomes and apply it to identify viral biomarkers of healthy oral and nasal microbiomes. Twelve viral biomarker candidates were selected from 1,232 curated viral operational taxonomic units. The viral biomarker candidates had as much as 63% prevalence across respiratory metagenomes, and prevalence was further increased to 77%-81% by combining two or three biomarkers. Real-time PCR confirmed that these viral biomarkers were prevalent and abundant in nasal swabs and saliva samples. Notably, top candidate biomarkers remained stable and detectable through multiple lab purification steps, increasing confidence in their viral origins and demonstrating their suitability for environmental monitoring. These findings demonstrate that existing metagenomes can be used to identify effective biomarker candidates for environmental sampling.IMPORTANCEDeveloping non-pharmaceutical interventions to reduce virus transmission indoors relies on robust environmental monitoring methods. Monitoring viral pathogens is challenging because of frequent non-detect measurements that introduce uncertainty. For instance, a non-detect measurement could indicate either the absence of the pathogen or simply the lack of human respiratory activity and, thus, exposure. To aid in distinguishing these scenarios, this study identifies viruses from the human respiratory tract using publicly available sequencing data that can be incorporated into environmental monitoring as biomarkers of human respiratory activity. These viral biomarkers will improve indoor monitoring to help enact interventions to mitigate virus transmission. Furthermore, our approach to identify biomarkers from existing metagenomes can be adapted for future biomarker identification in any system.
28 May 2026
Read appraisal →NEJM evidence
Outcomes of Pneumonia in ICUs in Low- and Middle-Income Countries - A Systematic Review
BACKGROUND: Community-acquired pneumonia (CAP) is a leading cause of intensive care unit (ICU) admission and death in low- and middle-income countries (LMICs). We conducted a systematic review and meta-analysis to estimate mortality among patients with CAP admitted to ICUs in LMICs. METHODS: We searched multiple databases using terms related to CAP admitted to the ICU and LMIC. We included both observational studies and clinical trials. The primary outcome was all-cause ICU, 28-day or 30-day mortality. Demographics, comorbidities, clinical characteristics, mechanical ventilation, and length of ICU stay were described. We registered the study in PROSPERO (CRD42022363048). RESULTS: A total of 52 studies from 18 countries met inclusion criteria, encompassing 48,707 patients. Most studies originated in upper-middle-income countries and no studies from low-income countries were identified. The most frequent comorbidities were hypertension, chronic obstructive pulmonary disease, and diabetes. Mechanical ventilation was reported in 36 studies, with median use in 59% (interquartile range, 41.4-77.4) of patients. Pooled mortality was 37% (95% confidence interval [CI], 31-42), rising to 61 (44-75) among patients requiring mechanical ventilation. In a meta-regression, age and mechanical ventilation were the strongest moderators of mortality, explaining 55.2% of heterogeneity in short-term mortality. CONCLUSIONS: Mortality among patients with CAP admitted to ICUs in middle-income countries remains high, especially in older and mechanically ventilated patients. The lack of data from low-income countries is notable.
27 May 2026
Read appraisal →Pharmaceutical biology
Ginger-based formulations for allergic rhinitis disease: a systematic review and meta-analysis of experimental studies in animals and humans
BACKGROUND: Allergic rhinitis is a chronic non-communicable disease that adversely affects respiratory health and quality of life. Ginger-based formulations, owing to their anti-inflammatory and immunomodulatory properties, may offer a promising alternative therapeutic option. OBJECTIVE: To systematically review and meta-analyze evidence from animal studies and human randomized controlled trials assessing the efficacy of ginger-based formulations in allergic rhinitis. METHODS: A systematic search of the Cochrane Library, Embase, PubMed, and Scopus (April 2025) identified studies comparing ginger-based formulations with control interventions. Outcomes included clinical symptoms and relevant biomarkers. Risk of bias was assessed using the SYRCLE tool for animal studies and RoB2 for human studies. Meta-analyses were conducted using random-effects models, with results expressed as standardized mean difference (SMD) and 95% confidence intervals (CIs). RESULTS: Twenty-one studies (6 animal and 15 human studies) were included. In rodents, ginger-based formulations significantly reduced nasal rubbing (SMD = -1.99; 95%CI, -3.15 to -0.83), sneezing (SMD = -1.99; 95%CI, -2.78 to -1.20), and biomarkers (IL-4, IL-5, and OVA-specific IgE). In humans, ginger-based formulations significantly improved itching (SMD = -0.51; 95%CI, -0.79 to -0.24), runny nose (SMD = -0.59; 95%CI, -0.87 to -0.30), sneezing (SMD = -0.56; 95%CI, -0.75 to -0.37), total nasal symptom scores (SMD = -0.63; 95%CI, -1.00 to -0.26), and quality of life (SMD = -0.56; 95%CI, -0.87 to -0.24), and eosinophil levels. CONCLUSION: Ginger-based formulations improve symptoms and biomarkers of allergic rhinitis in both animal and human studies. As many interventions are multi-component, the observed effects are more appropriately attributed to the overall formulations rather than ginger alone. Further research is needed to explore mechanisms and clarify clinical applications.
26 May 2026
Read appraisal →Circulation journal : official journal of the Japanese Circulation Society
A Phase 2, Randomized, Clinical Trial of Inhaled Nitric Oxide for Acute Severe Right Heart Failure With Pulmonary Hypertension (PHiNO Study)
BACKGROUND: Acute right heart failure (RHF) is life threatening in patients with pulmonary hypertension (PH). This study investigated the efficacy and safety of inhaled nitric oxide (iNO; INOflo®for inhalation 800 ppm), a rapid pulmonary vasodilator, in patients with acute severe RHF due to PH. METHODS AND RESULTS: In this Phase 2 randomized controlled trial, 30 patients with acute severe RHF due to PH (pulmonary arterial hypertension or chronic thromboembolic PH) were randomized 1 : 1 to either an iNO or control group (which did not receive iNO). The primary endpoint was the change in pulmonary vascular resistance (PVR) from baseline to 30 min after either iNO initiation or patient registration and assignment (control group), which differed significantly between the iNO and control groups (mean [±SD] -2.41±2.47 vs. 0.8±1.03 Wood units, respectively; between group difference -3.21 [95% confidence interval -4.633, -1.785] Wood units). Serum B-type natriuretic peptide (BNP) levels and inferior vena cava diameter (secondary endpoints) decreased significantly in the iNO group over the 7-day study period. No serious adverse events, including methemoglobinemia, were observed. CONCLUSIONS: iNO significantly reduced PVR in patients with acute severe RHF due to PH, without serious adverse events. Serum BNP levels and inferior vena cava diameter improved in the iNO group. These findings suggest that iNO is a promising acute treatment option for RHF due to PH.
25 May 2026
Read appraisal →The Science of the total environment
Ambient and personal air monitoring using low-cost sensors to identify elevated air pollution in vulnerable communities
In the United States, regulatory air monitoring networks are designed to assess regional air quality but do not capture neighborhood-level variability in pollutant concentrations. Low-cost air monitors may be one approach to address these gaps. The objective of this study was to evaluate whether neighborhood-level and personal monitoring identified increased concentrations of fine particulate matter (PM2.5) exposures in the neighborhood of Lower Price Hill (LPH), Cincinnati, Ohio, compared with regional regulatory monitors. We deployed a network of 12 PurpleAir monitors across LPH from April-June 2022 and calibrated measurements to a nearby U.S. EPA regulatory monitor. PM2.5 concentrations and Air Quality Index (AQI) values measured by the PurpleAir sensors were compared with two EPA monitoring sites. Additionally, three residents conducted 7-day personal monitoring using wearable AirBeam devices. Calibrated PurpleAir data demonstrated significantly increased PM2.5 concentrations at four of the 12 PurpleAir sites and increased AQI at 11 of the 12 PurpleAir sites in LPH compared to EPA regulatory sites. PurpleAir data also found that LPH experienced up to six days where AQI exceeded healthy levels (>50) but the EPA regulatory monitor did not exceed 50. Personal monitoring revealed substantial variability, with two of three participants exceeding the EPA 24-hour outdoor PM2.5 standard (>35 μg/m3) on all study days, indicating exposures not measured by outdoor stationary monitoring. Overall, regulatory monitoring may underestimate neighborhood-level and personal PM2.5 exposures in LPH. Community-based and personal monitoring provides novel insights into localized exposure disparities and may improve risk communication and inform interventions in vulnerable communities.
25 May 2026
Read appraisal →Environmental monitoring and assessment
XGBoost-based analysis of short- and long-term COVID-19 air quality impacts in Tehran, Iran.
This study examines the air quality trends associated with COVID-19 lockdowns and the subsequent recovery on air quality in Tehran, employing an established XGBoost framework for de-weathering with daily data from 22 monitoring stations from 2019 to 2025. A comprehensive analysis was conducted on six principal pollutants (PM₂.₅, PM₁₀, NO₂, O₃, SO₂, and CO) across four distinct temporal phases: Pre-pandemic, Pandemic, Post-COVID, and After Termination. While the model effectively reduced meteorological bias, exhibiting strong efficacy for particulate matter (R = 0.78-0.88; IOA = 0.68-0.88; FAC2 > 0.95), we explicitly acknowledge less consistent outcomes for reactive gases such as O₃ (R < 0.75; NMGE > 0.30). The imposition of lockdown measures coincided with significant reductions in pollutants associated with vehicular traffic: NO₂ exhibited a decline of - 30 to - 36%, PM₂.₅ decreased by - 18 to - 22%, and CO diminished by - 20%. In contrast, O₃ experienced an increase of + 20-30%, indicative of diminished NO titration. Principal Component Analysis (PCA) corroborated these trends, with PC1 (NO₂, PM₂.₅, CO) accounting for approximately 60% of the variance throughout the pandemic. In subsequent phases, partial recoveries were noted: PM₂.₅ escalated by + 15-20%, NO₂ stabilized at around -10%, SO₂ consistently decreased (-30% by 2025), whereas O₃ surged by + 20% by 2025, clustering with PM₁₀ in PC2, thereby highlighting the impacts of dust and secondary formation processes. Lockdowns yielded transient enhancements in air quality, with restricted long-term advantages. Sustainable progress necessitates the adoption of comprehensive policies targeting traffic, industrial emissions, VOCs, and dust management.
25 May 2026
Read appraisal →Computer assisted surgery (Abingdon, England)
An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC
Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcomes. However, STAS is frequently overlooked or misdiagnosed during routine pathological diagnosis. We analyzed 129 pathological slides from 91 STAS-positive patients with non-small cell lung cancer (NSCLC). An artificial intelligence framework consisting of a tumor region segmentation algorithm and an object detection algorithm was developed. The segmentation algorithm was first used to isolate the main tumor region, followed by STAS detection using an improved object detection model. The segmentation module achieved a Jaccard similarity of 0.846. The proposed object detection algorithm demonstrated superior performance with a precision of 0.738, recall of 0.747 and average precision (AP) of 0.784 and F1 score of 0.742, outperforming other object detection methods. Both segmentation and detection results met diagnostic requirements, with higher accuracy observed in lung adenocarcinoma (LUAD) than in lung squamous cell carcinoma (LUSC). In external validation, the model achieved a precision of 0.670, recall of 0.705 and AP of 0.732 and F1 score of 0.687. Additionally, the AI-derived STAS counts showed substantial agreement with pathologists (interclass correlation coefficient = 0.703). Kaplan-Meier survival analysis revealed that the number of STAS events was significantly associated with disease-free survival (DFS) in stage I LUAD (p < .05). We propose a deep learning-based artificial intelligence framework for automated STAS detection and quantification in digital whole-slide images of NSCLC. This model holds promising potential to assist pathologists in achieving more comprehensive and accurate diagnosis of STAS.
25 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Patterns of e-Cigarette Use and Smoking Cessation Outcomes: Secondary Analysis of a Large Randomised Controlled Trial to Inform Clinical Advice
INTRODUCTION: Limited data exist on how to use e-cigarettes (EC) to optimize smoking cessation. METHODS: We examined associations between patterns of EC use and outcomes at 1 year in a large trial (N = 886) comparing nicotine replacement therapy (NRT) and EC. RESULTS: Use of tobacco flavor was limited and associated with a lower smoking cessation rate compared to other flavors (relative risk; RR = 0.56, 95% CI = 0.35 to 0.89). EC users reduced nicotine strength over time. Abstainers using EC reported lower urges to smoke than abstainers using NRT at both weeks 1 (b = -0.25, 95% CI = -0.45 to -0.04) and 4 (b = -0.37, 95% CI = -0.58 to -0.16). Participants using both cigarettes and EC (dual users) at week 1 were more likely to stop smoking at week 4 than exclusive smokers (RR = 4.45, 95% CI = 1.96 to 10.10). Dual users at 4 weeks and 6 months were also more likely to achieve validated 50% reduction in smoke intake at 1 year (RR = 2.37, 95% CI = 1.36 to 4.11 and RR = 4.56, 95% CI = 2.71 to 7.66, respectively). CONCLUSIONS: Non-tobacco flavors were preferred and may be also more effective. Urges to smoke were lower in EC users than in users of NRT. Dual use was associated with a reduction in smoking and quitting smoking later on. IMPLICATIONS: Clinicians advising smokers wanting to use EC as a quitting aid can consider recommending non-tobacco flavors; explaining that EC reduce urges to smoke more than combination NRT; and reassuring those worried about dual use that such use is associated with reduced smoking and a higher chance of stopping smoking altogether later on.
25 May 2026
Read appraisal →Medicine
Applying digital technologies for remote care in the real life context: A 3-year experimentation with postoperative lung cancer patients
Surgery remains the main curative treatment for lung cancer patients. However, operation has resulted in some-degree reduction in postoperative pulmonary function. The decreased pulmonary function could be rehabilitated to the preoperative level through restoring exercise, which is related to the ways and exercise time of rehabilitating. The Dignio platform is 1 digital telemedicine system and has been used maturely in Norway, however, it has not yet been widely promoted in China. In the present study, we adopted the telemedicine system, according to the formulated rehabilitation exercise program, to design a good way to recover pulmonary function for postoperative lung cancer patients, promoting early and quick recovery, and reducing the long-term postoperative complications and outpatient visits. This retrospective controlled study enrolled 250 postoperative lung cancer patients using a remote digital care platform and 250 matched controls receiving standard instructions. In the postoperative 1-week follow-up, we found that the forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), forced expiratory volume in 6 seconds, FVC25-75, peak expiratory flow had an obvious upward trend, although there were no significant differences in the measurements on postoperative day 7 compared with day 1. From the long-term data at 1 month/3 months/6 months post-operation, we found that the recovery was better to varying degrees in the experiment group using the remote care digital platform, compared with that of the control, especially the FEV1 at 3 (P = .065) and 6 months post-operation (P = .037), and forced Expiratory Flow between 25% and 75% of forced vital capacity at 3 months post-operation (P = .084). However, most pulmonary values, such as the FEV1/FVC, FVC and peak expiratory flow have no significant difference (all P > .05) in the recovery at 1 month post-operation between the experimental group and control, the second time at 3 months and the third time at 6 months after surgery. Especially in the experimental group, 2 patients with sudden pneumothorax were timely detected after discharge through the Digino platform, the patient returned to the hospital in time, and both patients were out of danger ultimately. The Digino remote care indeed has some benefit in the pulmonary function recovery in the long-term follow up, and reduces postoperative complications obviously.
24 May 2026
Read appraisal →Scientific reports
A randomized controlled trial of adjunctive speleotherapy in asthma, COPD and long COVID
Speleotherapy (underground climate therapy) is a non-pharmacological intervention for chronic respiratory diseases. This randomized controlled trial investigated whether a 3-week speleotherapy course (6 sessions, 2 h/week) improves respiratory outcomes in patients on standard background therapy with asthma, COPD, or Long COVID, and whether it affects blood CO2 levels. The control group did not receive speleotherapy but continued their standard therapy. A total of 208 patients (asthma: n = 107; COPD: n = 59; Long COVID: n = 42) were enrolled across nine centers in Germany, Austria, and Italy. Assessments were conducted pre-intervention (T1), post-intervention (T2), and at 3-month follow-up (T3). Outcome measures included airway inflammation (FeNO), pulmonary function parameters (FVC% predicted values, FEV₁% predicted values, FEV₁/FVC, PEF% predicted values), and respiratory muscle strength (MIP and MEP in absolute values). In addition the following validated questionnaires were administered: Asthma Control Test (ACT), Asthma Quality of Life Questionnaire (AQLQ), COPD Assessment Test (CAT), St. George's Respiratory Questionnaire (SGRQ), Nijmegen Questionnaire (NQ), and Fatigue Assessment Scale (FAS), along with the Long COVID questionnaire from the Median Clinic Group. CO2 levels were assessed via capillary blood (SpCO2) and end-tidal CO2 (PetCO2). Between-group comparisons used the Mann-Whitney U test; within-group changes were assessed with the Wilcoxon signed-rank test (Bonferroni-Holm corrected). In patients with asthma, the predefined primary endpoint (FeNO) showed no significant improvement. In contrast, patient-reported outcomes improved significantly, with clinically relevant gains in asthma control (ACT: p < 0.001) and asthma-related quality of life (total AQLQ: p = 0.005). Lung function parameters showed statistically significant but modest improvements at T2 (FVC: p = 0.011; PEF: p = 0.010; FEV₁ in participants < 70 years: p = 0.035). In patients with COPD, symptom burden improved according to CAT scores (p = 0.036), while no improvements in lung function were observed. Patients with Long COVID reported significant improvements in dysfunctional breathing (NQ: T2: p = 0.014), dyspnea (T2: p = 0.026; T3: p = 0.001), and "problems with stair climbing/muscle exertion" (T2: p = 0.042), as well as improvements in anxiety and sleep-related symptoms (T2: p = 0.021). No improvements in lung function were observed in this group. In the total cohort, the intervention group showed statistically significant improvements compared to controls in respiratory muscle strength (MIP: p = 0.002; MEP: p = 0.018) and dysfunctional breathing (NQ scores at T2: p = 0.007; T3: p = 0.017). In CO₂-rich speleotherapy centers, both SpCO₂ (p = 0.026) and PetCO₂ (p < 0.001) increased at T2. While speleotherapy did not improve FeNO, it was associated with clinically relevant improvements in patient-reported outcomes across disease groups. Changes in lung function and respiratory muscle strength were statistically significant but modest and should be interpreted with caution. Overall, speleotherapy may have direct effects on the airways and breathing regulation, with more consistent evidence for improvements in breathing patterns than for direct effects on the airways.Trial registration: DRKS00033365 (retrospectively registered).
24 May 2026
Read appraisal →Medicine
Efficacy of traditional Chinese medicine in treating severe pneumonia: A meta-analysis and systematic review
BACKGROUND: Severe pneumonia (SP) is a critical respiratory disease characterized by its severity and high mortality rate. Traditional Chinese medicine (TCM) has gradually been used as an adjunctive therapy in clinical treatment of SP, yet its efficacy and safety lack systematic evidence. This study aims to analyze, evaluate, and compare current research evidence on TCM adjunctive therapy for SP, providing a basis for clinical treatment of this condition. METHODS: A systematic search was conducted on both English and Chinese online databases (PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP) to identify relevant papers published from inception to November 2023. High-quality randomized controlled tests (RCTs) were selected for analysis. Outcome measures related to SP were extracted from the literature. Statistical analysis was performed using RevMan 5.3 (Cochrane) software to assess heterogeneity among included studies, calculate mean differences, odds ratios, and their 95% confidence intervals, and detect publication bias. RESULTS: A total of 34 RCTs met the inclusion criteria. Comparative analysis revealed that integrating TCM with conventional treatment significantly improves the overall clinical effective rate(P < .05). Specifically, it markedly alleviates TCM symptoms (P < .05). Additionally, TCM adjunctive therapy reduces levels of inflammatory markers including white blood cells, C-reactive protein (P < .05), and decreases Clinical Pulmonary Infection Score and Acute Physiology and Chronic Health Evaluation II scores compared to conventional treatment alone (P < .05). Compared to conventional treatment alone, TCM adjunctive therapy also enhances arterial blood gas indices (PaO2) (P < .05) and pulmonary function (forced expiratory volume in 1 second) (P < .05). Complication rates and mortality are lower in the TCM adjunctive therapy group(P < .05). Moreover, TCM treatment significantly boosts immune function (cluster of differentiation 4+ T lymphocyte) (P < .05). CONCLUSION: Integrating TCM with conventional treatment shows advantages in the efficacy of SP treatment, clinical symptoms management, inflammatory markers, and severity grading. Compared to conventional treatment alone, TCM adjunctive therapy improves immune function, pulmonary function, complication rates. However, future research should focus on large-scale, high-quality RCTs to further validate the effectiveness and safety of TCM adjunctive therapy for SP.
24 May 2026
Read appraisal →Journal of medical Internet research
Reporting of Telehealth Implementation in Cystic Fibrosis: Scoping Review Using a Novel Theory-Based Evaluation Lens
BACKGROUND: Many inductive reviews exploring telehealth and its application in health care have identified missing or inconsistently reported implementation data, calling for a standardized approach to telehealth research. OBJECTIVE: Using cystic fibrosis (CF) as a case exemplar, this study evaluated the adherence of telehealth research to standardized reporting frameworks through a theory-based evaluation lens to assess implementation reporting quality and identify knowledge gaps and strengths across the literature. METHODS: We conducted an updated systematic review of the PubMed, Scopus, and Web of Science databases using a novel deductive approach to identify relevant scientific papers available in English and focusing on the delivery of telehealth interventions to CF populations as part of or alongside routine CF care. Two relevant reporting checklists were identified in the Equator Network database (Guidelines and Checklist for the Reporting on Digital Health Implementations [iCHECK-DH] and Template for Intervention Description and Replication for Telehealth [TIDiER-telehealth]) to extract data from the papers. Each checklist category was described as being "fully reported" (score=2), "partially reported" (score=1), and "did not report" (score=0) for each paper. An overall score was calculated for adherence to the checklists. RESULTS: In total, 98 studies published between 2006 and May 2025 were included in this review, with the majority appearing during the COVID-19 pandemic (2021-2022). Most studies were conducted in a single country, predominantly the United States, Australia, and the United Kingdom, and were published in medical journals. Telehealth was variably described, with video call-based models in combination with remote monitoring being most common. The median score was 22/40 (range 11-29, 55.0% adherent) for iCHECK and 15/24 (range 6-23, 62.5% adherent) for TIDiER, demonstrating moderate overall reporting quality. For iCHECK, ≥50% of studies fully reported 6/20 categories, partially reported 9/20 categories, and did not report 3/20 categories. For TIDiER, ≥50% of studies fully reported 4/12 categories, partially reported 6/12 categories, and did not report 1/12 categories, indicating persistent gaps in intervention description despite improved partial reporting. CONCLUSIONS: Key areas, such as justification for telehealth, target populations, and outcomes, are well documented, offering valuable insights into the rationale for and outcomes associated with telehealth. However, implementation processes remain underreported, partly due to the more recent adoption of frameworks like iCHECK and TIDiER. The clinical implications of the current evidence limit the implementation of telehealth in terms of the ability to assess feasibility and readiness for adoption; understand financial implications and plan sustainably; ensure patient safety, data protection, and equity; interpret outcome data in context; and share, replicate, or scale evidence-based models of care. Strengthening the commitment to standardized telehealth reporting will ultimately support clinical decision-making and improve the effective and equitable integration of telehealth into care.
23 May 2026
Read appraisal →Lancet (London, England)
Safety and efficacy of astegolimab for COPD with frequent exacerbations regardless of baseline blood eosinophil counts (ALIENTO and ARNASA): randomised, double-blind, placebo-controlled, phase 2b and 3 trials
BACKGROUND: Interleukin-33 and its receptor, ST2, are implicated in neutrophilic and eosinophilic inflammation during chronic obstructive pulmonary disease (COPD) exacerbations. We aimed to assess the efficacy and safety of astegolimab, an anti-ST2 human IgG2 monoclonal antibody, which were evaluated in two COPD pivotal trials. METHODS: In two randomised, double-blind, placebo-controlled trials (phase 2b ALIENTO and phase 3 ARNASA), current or former smokers with COPD and a history of frequent exacerbations, irrespective of baseline blood eosinophils, were randomly assigned (1:1:1; stratification by smoking status and region) to receive subcutaneous astegolimab 476 mg every 2 weeks, every 4 weeks, or placebo, alongside optimised inhaled maintenance therapy over 52 weeks. The primary endpoint (analysed in participants receiving one or more doses) was annualised rate of moderate or severe exacerbations. Missing data were considered similar to data from other participants in the same treatment group with the same baseline characteristics. The trials were registered with ClinicalTrials.gov (NCT05037929 and NCT05595642). FINDINGS: In ALIENTO, 1301 participants (astegolimab every 2 weeks, n=433; astegolimab every 4 weeks, n=437; and placebo, n=431) initiated treatment between Oct 5, 2021, and Feb 19, 2024. In ARNASA, 1375 participants (astegolimab every 2 weeks, n=459; astegolimab every 4 weeks, n=459; and placebo, n=457) initiated treatment between Jan 9, 2023, and June 25, 2024. Adjusted rate ratios versus placebo for the primary endpoint were 0·85 (95% CI 0·72-1·00; p=0·049) for astegolimab every 2 weeks and 0·93 (0·79-1·10; p=0·38) for astegolimab every 4 weeks in ALIENTO, and 0·85 (0·72-1·01; p=0·068) for astegolimab every 2 weeks and 0·82 (0·70-0·98; p=0·024) for astegolimab every 4 weeks in ARNASA. Adverse events were balanced between treatments, with most participants experiencing one or more adverse events (1093 [84·0%] of 1301 participants in ALIENTO and 1176 [85·5%] of 1375 in ARNASA). The most common non-COPD adverse event was nasopharyngitis in ALIENTO and upper respiratory chest infection in ARNASA. Deaths occurred in 40 (3·1%) of 1301 participants in ALIENTO and in 44 (3·2%) of 1375 participants in ARNASA, and were balanced across treatment groups. Across both trials, a total of three deaths (0·1%) were considered to be related to treatment by investigators. INTERPRETATION: In ALIENTO, astegolimab every 2 weeks was associated with a lower annual rate of exacerbations versus placebo in patients with COPD and a history of frequent exacerbations. In ARNASA, these findings did not meet statistical significance. Together, these findings suggest a role for targeting the ST2/IL-33 pathway to reduce the frequency of COPD exacerbations in patients with limited treatment options. FUNDING: Genentech, a member of the Roche Group, and F Hoffmann-La Roche.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Mediators of the Effect of Opt-In Versus Opt-Out Treatment on Smoking Cessation in a Randomized Clinical Trial
INTRODUCTION: Smoking cessation interventions are effective. Most smokers do not receive them, in part because smokers are required to "opt-in". Changing the default (CTD) randomized clinical trial, conducted at a large academic medical center, demonstrated that CTD to providing treatment unless refused (opt-out) improved quit rates. The objective of this study was to evaluate if treatment participation and default-theory based variables explained the effect of opt-out on cessation. METHODS: We conducted a secondary analysis of CTD data (N = 739). Descriptive statistics and correlations of the baseline variables were provided. Structural equation models were employed to investigate whether the treatment assignment was causally mediated by a priori proposed mediators including perceived endorsement, counseling participation, and medication utilization. RESULTS: The mean age of the participants was 51.39 (SD = 14.71) years, among whom 349 (47.23%) were females, and the majority was White (428, 57.92%). The indirect effects from the opt-out treatment arm to counseling participation to smoking cessation (b = 0.201, 95% CI = 0.105 to 0.303), and from opt-out to perceived endorsement to counseling participation to smoking cessation (b = 0.010, 95% CI = 0.002 to 0.022) were significant. The indirect effect from opt-out to perceived endorsement to counseling participation to medication utilization to smoking cessation was marginally significant (b = .001, 95% CI = 0.000 to 0.003). CONCLUSIONS: The opt-out approach enhanced patients' sense that their counselor endorsed and/or recommended treatment, as well as counseling participation. The results partially support our hypothesis that the advantage of Opt-out care would increase treatment participation and would do so by psychological variables thought to underpin the efficacy of defaults. IMPLICATIONS: Our results strengthen evidence for the value of delivering smoking cessation as a default (opt-out rather than opt-in) by illuminating likely mechanisms of action. These include increasing participation in counseling and two default-based theoretical mechanisms, perceived endorsement and implied endorsement.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model
INTRODUCTION: The most effective way to reduce mortality and morbidity among current smokers is to quit smoking. Although about half of smokers attempted to quit, only one-tenth succeeded in 2022. Understanding key predictors of smoking cessation success would inform smoking cessation interventions and increase quitting rates. METHODS: We analyzed data from waves 5 and 6 of the Population Assessment of Tobacco and Health (PATH) study (December 2018 to November 2021). Using OpenAI's GPT-4.1, we identified the top 45 variables from wave 5 that are highly predictive of 12-month smoking abstinence in wave 6, based on descriptions of survey variables. We then validated the predictive power of the GPT-4.1-selected variables by comparing the performance of eXtreme Gradient Boosting (XGBoost) trained on different sets of variables. Finally, we derived insights into the top 10 variables, ranked according to their SHapley Additive exPlanations values. RESULTS: The performance of XGBoost trained with all possible wave 5 variables and the 45 selected variables was almost identical (AUC:0.749 vs AUC:0.752). The top 10 variables included past 30-day smoking frequency, minutes from waking up to smoking first cigarette, important people's views on tobacco use, prevalence of tobacco use among close associates, daily electronic nicotine product use, emotional dependence, and health harm concerns. CONCLUSIONS: The high predictive performance of XGBoost, when trained on the selected variables, underscores the efficiency and efficacy of GPT-4.1-based feature selection. The top 10 variables include various risk factors that have been previously reported in the literature for their influence on smoking behavior. IMPLICATIONS: Our findings do not establish causal relationships between the selected predictors and 12-month smoking abstinence. However, identifying these key predictors provides valuable insights into the factors highly associated with smoking cessation success. This study demonstrates the ability of OpenAI's GPT-4.1 to perform feature selection using only the textual descriptions of variables. The efficient and successful application of GPT-4.1 for variable selection highlights the potential of integrating artificial intelligence tools into tobacco research to guide resource-efficient and targeted intervention strategies.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Acceptability of Using Large Language Models to Support Smoking Cessation Attempts: a Qualitative Study Among African Americans Who Smoke
INTRODUCTION: African Americans face significant disparities in tobacco-related diseases and access to smoking cessation resources. Digital health interventions using large language models (LLMs) show promise in increasing engagement and successful quit attempts. However, impressions regarding acceptability of LLM-based interventions among African Americans are unknown and evidence suggests such interventions require tailoring for underserved communities. Our goal was to evaluate perceptions of LLM-based interventions among African Americans to inform development of an LLM-based chatbot to support quit attempts. METHODS: We conducted remote focus groups with adult African Americans residing in Minnesota who smoke cigarettes to determine acceptability of LLM-based health interventions and inform the functionality of an LLM-based chatbot supporting quit attempts. Discussions followed a structured guide and were recorded to facilitate analysis. Transcripts were coded independently by two analysts with a shared codebook. RESULTS: 21 adults participated in focus group discussions. 57.1% of participants were female and the mean age was 45.6 (SD, 10.5). Three themes united participants' feedback: (1) participants limited familiarity with LLM and related AI technology, (2) willingness to use an LLM-based app to support quit attempts, and (3) perceptions of features that impact the acceptability of an LLM-based chatbot to support quit attempts. CONCLUSION: African Americans have complex perceptions and expectations regarding the use of LLMs to deliver health interventions. While many recognize the potential benefits, significant concerns and knowledge gaps remain. To build trust and acceptability, intervention developers should prioritize community engagement and ethical guidelines to ensure that LLM-driven solutions are inclusive and effective for multiple communities. IMPLICATIONS: Large language models and related AI technologies have shown promise for improving access and engagement when deployed through digital health interventions. Focus groups with African Americans who smoke combustible cigarettes revealed factors impacting the acceptability in using LLM-based interventions to support quit attempts. Efforts to build digital health interventions should prioritize community engagement to reduce apprehensions and improve alignment with community needs and values.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
The Public Health Impacts of Flavored Cigar Bans: A Systematic Review and Meta-Analysis
INTRODUCTION: This review summarizes research on the public health impacts of flavored cigar bans, which are policies that restrict the sale of cigars containing characterizing flavors. METHODS: A systematic literature search was conducted using PubMed, Scopus, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, and Applied Social Sciences Index and Abstracts. Studies were included if they evaluated the impact of a real-world or hypothesized flavored cigar ban. The primary outcome was tobacco use behavior. Secondary outcomes included retailer compliance, sales, and the tobacco industry response to flavored cigar bans. Two reviewers independently screened articles for inclusion, extracted data, and assessed study quality. We also conducted a meta-analysis using random effects models to pool estimates on tobacco use behavior after a flavored cigar ban. This review was registered with PROSPERO (CRD42023481603). RESULTS: Of 2,399 articles identified in the preliminary search, 55 were included in the review. Many real-world studies found declines in cigar use post-ban, specifically among youth. However, other studies found no significant changes in cigar use. Among studies examining sales, there were declines in flavored cigar sales but increases in sales of non-flavored cigars and other tobacco products. Among studies assessing hypothesized tobacco use behavior, pooled results indicate that 8% (95% CI: 2%, 27%) of flavored cigar users intend to quit tobacco altogether, 45% (95% CI: 39%, 50%) would switch to non-flavored cigars or cigarettes, and 41% (95% CI: 32%, 51%) would switch to other tobacco products. CONCLUSIONS: Flavored cigar bans may reduce cigar use and improve public health when policies are comprehensive and well-enforced. IMPLICATIONS: Overall, findings from this review suggest that flavored cigar bans may reduce tobacco use and have the potential to improve public health, but not all research is consistent. Several studies examining the impacts of flavored cigar bans on tobacco use found a decrease in cigar use among youth, but some found that there was no significant change in cigar use. Research on policy impacts among adults is limited. To maximize intended policy impact, monitoring of retailer compliance and the tobacco industry's response to flavored cigar bans is needed.
23 May 2026
Read appraisal →European journal of pediatrics
Application of machine learning to predict bronchiolitis severity in children: a single-centre retrospective cohort study
UNLABELLED: Several severity scores have been developed to assess disease severity in infants with bronchiolitis, but they often lack objectivity and may not reliably reflect clinical outcomes. We performed a single-centre retrospective cohort study including infants younger than 12 months admitted with a clinical diagnosis of bronchiolitis over three epidemic seasons (2021-2024). Cluster analysis was applied to identify groups of infants with similar clinical patterns, which were subsequently mapped to severity levels. A machine learning (ML) model was then trained to predict these severity levels using anamnestic and clinical variables available at admission. For comparison, the bronchiolitis risk of admission score (BRAS) and the respiratory severity score-heart rate (RSS-HR) were also calculated at admission. A total of 112 infants were included, and three severity levels were identified. Mild bronchiolitis corresponded to infants not requiring NICU admission or supplementary ventilation. Moderate cases involved infants needing supplementary ventilation without NICU admission. Severe bronchiolitis was associated with both NICU admission and supplementary ventilation. BRAS showed only a weak association with the newly defined clinical severity score (τ = 0.354, p < 0.001), while RSS-HR showed no significant correlation. In contrast, the ML model demonstrated strong predictive performance (τ = 0.731, p < 0.001 in the test set). A simplified decision tree based on three admission variables-age, retractions, and oxygen saturation-also showed good predictive ability (τ = 0.654, p < 0.001). CONCLUSIONS: A new dynamic approach for assessing bronchiolitis severity at hospital admission, capable of robust and adaptable prediction across clinical settings, has been elaborated. WHAT IS KNOWN: • Several bronchiolitis severity scores exist, but many lack validation, are subjective, and have limited applicability across settings, making early and accurate severity prediction difficult. WHAT IS NEW: • Our study introduces a three-level, outcome-based severity score and a dynamic ML tool, plus a simplified ARS decision tree, all outperforming conventional scores in predicting severity at admission.
23 May 2026
Read appraisal →ACS sensors
Moisture-Gated Bio-Semiconductor Electronic Tattoos for Continuous and Imperceptible On-Skin Respiratory Monitoring
Continuous respiratory monitoring remains one of the most critical yet unmet needs in personal and clinical healthcare. To overcome the limitations of cumbersome systems and motion-prone wearables, we present a moisture-gated bio-semiconductor-based electronic tattoo (BSET) that enables imperceptible, skin-conformable, and wireless respiratory monitoring by leveraging the hydration-sensitive ionic-electronic conductivity of melanin and the skin-compatibility of silk. Fabricated from a silk fibroin-melanin nanofiber bio-composite, the BSET is ultrathin at 18 μm and highly breathable, achieving a water vapor transmission rate exceeding 3000 g·m-2·d-1. By detecting exhaled moisture directly on the philtrum, the sensor exhibits a rapid response time of 1 s and a recovery time between 2 and 10 s. A direct-spun nanofiber-based wiring strategy ensures robust integration, accommodating a 10 mm displacement under 10.4 MPa of stress without failure. Driven by a compact 3 g circuit with 20 mW of power consumption, the system supports continuous wireless data transmission for over 7.3 h. During vigorous exercise and sleep, the BSET reliably monitored respiratory dynamics, identifying 10-20 s apnea events and enabling multiparameter analysis of breathing frequency and exhalation intensity. This lightweight system establishes a scalable and clinically relevant solution for continuous respiratory surveillance.
23 May 2026
Read appraisal →Current environmental health reports
Industrial Pollution and Health in Louisiana: A Systematic Review of Quantitative and Qualitative Studies
PURPOSE OF REVIEW: Louisiana has one of the largest concentrations of petrochemical industry in the USA. Many studies have assessed patterns of industrial pollution and health in Louisiana; we aim to systematically review this evidence. We systematically searched PubMed, Web of Science, Embase, APA PsycInfo, and GreenFILE for peer-reviewed papers published 1999-2024 that reported geographical variation in health or industrial pollution, and/or tested for an association between the two in Louisiana. We used Covidence to support standardized review and extraction. RECENT FINDINGS: We identified 2485 non-duplicate papers in our search; 53 met the inclusion criteria. Most reported quantitative findings. All studies of industrial pollution described air pollution (some also described other pollution). Studies described various health outcomes, including cancer, respiratory health, mortality, and COVID-19. Overall, people who lived closer to industrial activity had higher pollution exposure and worse health. Black and lower-income residents were exposed to more industrial activity than white and higher-income residents. Twenty-one studies assessed statistical associations between industrial pollution and health; many found an association. Twenty-one studies were quantitative and adjusted for confounding, 29 studies did not adjust for confounding (including qualitative studies), and three studies did not adjust for confounding and had authors with industry ties. Evidence suggests that there is a higher burden of air pollution and worse health outcomes in industrialized areas of Louisiana. While there was some evidence of significant associations between industrial pollution and health outcomes, research with larger sample sizes and improved pollution exposure measurements could be informative.
23 May 2026
Read appraisal →JMIR formative research
MyLungHealth, a Patient-Facing Education Tool for Lung Cancer Screening: Qualitative User-Centered Design Study
BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, with low-dose computed tomography screening demonstrating an approximately 20% reduction in mortality among high-risk individuals. Despite this benefit, screening prevalence remains suboptimal, with often less than 20% of eligible individuals reported to be up to date on screening. Shared decision-making is essential for effective lung cancer screening (LCS) implementation, with decision aids shown to enhance patient knowledge and engagement. OBJECTIVE: The aim of this study is to identify patient preferences, concerns, and design considerations through qualitative evaluation of MyLungHealth, a personalized patient-facing educational tool for LCS integrated with electronic health records, and to describe how these findings informed iterative design modifications. METHODS: We employed qualitative research methods through focus groups (n=34) and individual interviews (n=18) with individuals who met screening eligibility criteria. Participants were recruited from the University of Utah Health and New York University Langone Health between May and December 2023. Feedback was analyzed using Braun and Clarke's thematic analysis principles. RESULTS: Six themes were organized into three overarching domains. Domain A included interpretation and impact of personalized risk information: theme 1, difficulties interpreting risk information, and theme 2, varied impacts of risk information on motivation. Domain B included autonomy, privacy, and user interface preferences: theme 3, desire for autonomy and control over personal health data, and theme 4, preference for straightforward language and multiple information formats. Domain C included integration with clinical workflows and patient portal systems: theme 5, expectations for integration with health care provider workflows, and theme 6, mixed experiences with personal health record systems. These insights led to key design modifications, including simplified risk presentation, multimodal content delivery options (video and text), and implementation of electronic health record alerts for clinicians. CONCLUSIONS: The user-centered design process for MyLungHealth revealed important considerations for developing effective patient education tools for LCS. The findings highlighted the need for simplified risk presentation, personalized information delivery, and integration with clinical workflows. These findings underscore the importance of balancing comprehensive risk communication with user accessibility.
23 May 2026
Read appraisal →BMJ paediatrics open
Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.
BACKGROUND: Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromic surveillance tools, including the WHO Severe Acute Respiratory Infection definition, demonstrate limited diagnostic accuracy in children whose symptom profiles vary widely. This study aimed to develop a machine learning (ML) model to predict microbiologically confirmed vRTIs in hospitalised children and to evaluate performance across age groups and viral pathogens. METHODS: We conducted a retrospective cross-sectional study of 2050 paediatric patients (<18 years) admitted with acute respiratory infections to two tertiary paediatric hospitals in Canada. Predictors included age, sex, hospital transfer status, chronic comorbidity status and 22 presenting symptoms. The primary outcome was microbiologically confirmed vRTI, determined by multiplex PCR or rapid antigen testing. Six ML algorithms were trained and the best-performing model, identified by area under the receiver operating characteristic curve (auROC), was tested on age subgroups, viral pathogens and sites. RESULTS: Among 2050 patients (median (IQR) age 2.4 (0.8-5.2) years), 1831 (89.3%) tested positive, most commonly for respiratory syncytial virus (RSV) (38.7%) and enterovirus/rhinovirus (32.8%). Logistic regression with L2 regularisation demonstrated the best performance (auROC, 0.754; 95% CI 0.697 to 0.808; sensitivity, 69.2%; specificity, 69.9%), with greatest performance among children <1 year (auROC, 0.763) and RSV cases (auROC, 0.727). CONCLUSIONS: An ML-based logistic regression model using admission data accurately predicted paediatric vRTIs, outperforming traditional syndromic surveillance definitions, especially among infants <1 year. By integrating ML models into hospital electronic medical records, healthcare systems can achieve enhanced respiratory virus surveillance, faster outbreak detection, greater diagnostic efficiency and improved pandemic preparedness.
23 May 2026
Read appraisal →BMJ evidence-based medicine
Comparative effects of early physical interventions on preventing intensive care unit-acquired weakness: a systematic review and component network meta-analysis
OBJECTIVE: To compare the effects of early physical interventions on the prevention of intensive care unit-acquired weakness (ICUAW) and the improvement of relevant clinical outcomes in patients with critical illness. METHODS: We systematically searched the Web of Science, PubMed, Embase and the Cochrane Central Register of Controlled Trials from their inception until 20 August 2024, to identify randomised controlled trials (RCTs) enrolling patients ≥18 years old and implementing early physical intervention that commenced at any time point during mechanical ventilation (MV) use or within 7 days after intensive care unit (ICU) admission for review. We synthesised data using a random-effects model and analysed through network meta-analysis (NMA) and component network meta-analysis (CNMA). MAIN OUTCOME MEASURES: Primary outcome is the incidence of ICUAW. Secondary outcomes included Medical Research Council sum score, length of stay in the ICU or hospital, duration of MV and mortality rates in the ICU or hospital. RESULTS: Our analyses included 63 RCTs involving 24 treatments and eight components. The NMA results revealed systematic early mobilisation (SEM) combined with neuromuscular electrical stimulation (NMES), SEM alone and NMES alone may lead to a moderate to large reduction in the incidence of ICUAW (odds ratios [ORs]=0.03, 0.09 and 0.12, 95% confidence intervals [CIs]=0.00 to 0.42, 0.01 to 0.97 and 0.03 to 0.44, respectively) and improved relevant clinical outcomes compared with routine care. The CNMA results further indicated that SEM (OR=0.14, 95% CI=0.02 to 0.83) and NMES (OR=0.22, 95% CI=0.09 to 0.52) effectively mitigated the ICUAW incidence. CONCLUSIONS: SEM and NMES are optimal interventions for preventing ICUAW. Healthcare providers in ICUs should implement early mobilisation with structured protocols and patient assessments or apply NMES to specific muscle groups to prevent ICUAW in critically ill patients and improve relevant clinical outcomes. PROSPERO REGISTRATION NUMBER: CRD42024581173.
23 May 2026
Read appraisal →JMIR research protocols
Mobile Health Technology for Personalized Tobacco Cessation Support in Laos (Project Support Laos): Protocol for a Randomized Controlled Trial
BACKGROUND: Tobacco use remains the leading cause of preventable morbidity and mortality in Lao People's Democratic Republic (Lao PDR). Despite the prevalence of cigarette smoking in Lao PDR (51% in men and 7% in women), no national tobacco treatment programs are available. Therefore, the development and evaluation of sustainable tobacco cessation interventions suitable for widespread adoption in Lao PDR are pressing public health needs. OBJECTIVE: This project aims to adapt our theoretically and empirically based mobile health (mHealth) technology to help people quit smoking cigarettes in Lao PDR. METHODS: Our mHealth approach includes a fully automated, interactive, personalized, smartphone-delivered intervention for behavioral treatment. This project includes 2 main phases. In the first phase, we use formative research methods to adapt our intervention content to the sociocultural context, language, and communication styles of Laotians. In the second phase, we conduct a randomized controlled trial to evaluate the efficacy of our mHealth intervention. In the trial, adult smokers are recruited through 2 national hospitals: Setthathirath Hospital in Vientiane and Champasak Hospital in Champasak Province. Participants (n=500) are randomized to either the standard care (SC; n=250) or automated treatment (AT; n=250) group. SC consists of brief advice to quit smoking delivered by research staff, self-help written materials, and a 2-week supply of nicotine replacement therapy (transdermal patches). AT consists of all SC components plus a fully automated smartphone-based treatment program that involves interactive and personalized proactive messages, images, or videos. The primary health outcome of the trial is biochemically confirmed self-reported 7-day point prevalence abstinence 12 months post study enrollment. Secondary outcomes include abstinence at 3 and 6 months post enrollment. RESULTS: This study was approved by the ethical review boards of the respective domestic and international institutions. Data collection for the formative phase occurred from January 2022 to May 2023, and data analyses are ongoing. Data collection for the trial phase is ongoing and is expected to be completed by the end of 2026. CONCLUSIONS: If the proposed project is successful, it has the potential to transform health care services for tobacco treatment throughout Lao PDR and, ultimately, to significantly reduce tobacco-induced morbidity and mortality in the country. The AT's potential for widespread adoption and sustainability is enhanced by the direct involvement of Lao governmental stakeholders at multiple national institutes. Furthermore, the US-Lao collaborative work and capacity-building activities in this project will contribute to creating a knowledge base for mHealth research applications and advancing mHealth research capacity in Lao PDR.
22 May 2026
Read appraisal →PloS one
Double-cuff versus single-cuff bronchial blockers for video-assisted thoracoscopic lobectomy: A randomized controlled trial
BACKGROUND: Bronchial blockers (BBs) manage airways during thoracoscopic lobectomy and isolate lungs by occluding the bronchus at the surgical site. However, intraoperative malpositioning remains a concern. We compared the performance of a new double-cuff BB (DcBB) with an additional cuff near the end of a single-cuff BB (ScBB) with that of ScBBs during thoracoscopic lobectomy. METHODS: This single-center, randomized, parallel, controlled clinical study enrolled 80 patients undergoing thoracoscopic lobectomy for lung cancer at Nanpi People's Hospital. The patients were randomized into two groups (n = 40 each); DcBB (DcBB used during one-lung ventilation) and ScBB (ScBB used during one-lung ventilation). The primary outcomes were the incidence and number of BB malpositioning. Secondary outcomes time for BB placement and positioning, surgical duration, hypoxemia, incidence of adverse cardiovascular events, grades of lung collapse, surgeon satisfaction, tracheal wall damage assessed during bronchoscopy, contamination rates of the non-surgical lung lobe on the surgical site determined by bronchoscopy, radiographic incidence of pulmonary infiltration on postoperative day 1, and intraoperative blood gas values. RESULTS: The incidence of malpositioning was significantly lower in the DcBB than the ScBB group (7.5% vs. 30%, P = 0.02). The incidence of malposition was significantly lower in the DcBB cohort (P = 0.002). However, positioning the DcBB required more time (32.28 ± 5.37 vs. 19.90 ± 4.16 sec, P < 0.001). Other secondary outcomes did not differ significantly. CONCLUSIONS: In patients undergoing thoracoscopic lobectomy, the DcBB was associated with a lower incidence of malposition than the ScBB. The DcBB did not increase tracheal wall injury or cause significant adverse hemodynamic effects, and therefore may serve as a promising optional device for airway management during thoracoscopic lobectomy, particularly in clinical scenarios requiring high placement stability. TRIAL REGISTRATION: the Chinese Clinical Trial Registry ChiCTR2400086472.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Safety and Immunogenicity of a Fourth Dose of Omicron BA.1-Adapted BNT162b2 COVID-19 Vaccines in Adults 18-55 Years Old
BACKGROUND: Emergence of severe acute respiratory syndrome coronavirus 2 sublineages warrants the use of sequence-adapted vaccines to provide protection against coronavirus disease 2019. METHODS: In this phase 3 trial, adults 18‒55 years old who had previously received three 30 μg doses of BNT162b2 vaccine were randomized to receive a 60 or 30 μg dose of bivalent Omicron BA.1‒adapted BNT162b2 comprising equal amounts of ancestral and monovalent messenger RNA BA.1 (bivalent BA.1) or a 60 μg dose of monovalent Omicron BA.1‒adapted BNT162b2 (monovalent BA.1). Safety (local reactions, systemic events, adverse events [AEs], and serious AEs) was the primary objective. Exploratory analyses assessed immune responses against Omicron BA.1, BA.4, and BA.5 subvariants and ancestral strain. RESULTS: Among the 1054 randomized participants who received monovalent BA.1 or bivalent BA.1, frequencies of local reactions, systemic events, and AEs were slightly higher with 60 µg monovalent BA.1 than either bivalent BA.1 dose level. One month after vaccination, bivalent BA.1 (30 and 60 μg) and monovalent BA.1 (60 μg) induced substantial neutralizing responses against Omicron BA.1 (50% neutralizing titer geometric mean fold rises [GMFRs], 15.4 [95% CI 12.4-19.2], 17.1 [13.7-21.4], and 24.6 [19.3-31.4], respectively) and ancestral strain (GMFRs, 6.2 [5.1-7.6], 7.3 [6.0-8.9], and 7.0 [5.7-8.7], respectively). In a smaller (n = 30/treatment arm) sentinel cohort, all study vaccines modestly neutralized Omicron BA.4 and BA.5. CONCLUSIONS: Bivalent and monovalent BA.1‒adapted vaccines had a safety profile similar to the original BNT162b2 30 μg and induced substantial neutralizing responses against Omicron BA.1 and ancestral strains. CLINICAL TRIALS REGISTRATION: NCT04955626.
22 May 2026
Read appraisal →BMJ open respiratory research
Symptom-based rehabilitation in people with post-COVID-19 condition (RELOAD study): a randomised controlled trial
RATIONALE: Post-COVID condition (PCC) is characterised by persistent symptoms that impair health-related quality of life (HRQoL); rehabilitation (REHAB) strategies remain scarce. OBJECTIVE: To investigate the effect of a symptom-based REHAB programme versus usual care on HRQoL in people with PCC. METHODS: In this single-blind randomised controlled trial, a total of 132 people with PCC were randomised to either a REHAB group or a control group, with 104 completing the trial. The REHAB group received a multimodal 3-week REHAB programme tailored to the participant's primary symptom-based group (fatigue, cognitive problems or somatic symptoms); the control group received usual care. The primary endpoint was the between-group difference in change in HRQoL, measured by the 12-item Short Form Health Survey (SF-12) physical component summary (PCS) score at 3 weeks post baseline (intervention period). Changes from baseline to a 3-month follow-up after the intervention period were also analysed as a secondary measure. Additional outcomes included mental HRQoL (SF-12 mental component summary (MCS)), depression symptoms (Patient Health Questionnaire 9 (PHQ-9)) and anxiety symptoms (Generalized Anxiety Disorder 7 (GAD-7)). RESULTS: Participants (mean age 47±12.3 years, 90.4% not hospitalised during acute SARS-CoV-2 infection, 69.2% female) in the REHAB group (n=47) showed a significantly greater improvement in PCS compared with the control group (n=57) (mean difference (Δ) 2.9 points, 95% CI 0.2 to 5.7, p=0.038) at 3 weeks post baseline. This difference remained significant at the 3-month follow-up (Δ 4.2 points, 95% CI 1.5 to 6.9, p=0.003). The REHAB group also showed significant improvements in mental HRQoL (MCS: Δ 5.7 points, 95% CI 2.0 to 9.4, p=0.003), as well as in depression symptoms (PHQ-9: Δ -2.7 points, 95% CI -4.2 to -1.2, p<0.001) and anxiety symptoms (GAD-7: Δ -2.4 points, 95% CI -3.7 to -1.1, p<0.001) at 3 weeks post baseline compared with the control group. At the 3-month follow-up, the reduction in depression symptoms remained significant, while no significant differences were observed for mental HRQoL or anxiety symptoms. CONCLUSION: A symptom-based rehabilitation programme significantly improved HRQoL in individuals with PCC after 3 weeks, with some benefits persisting at 3 months. TRIAL REGISTRATION NUMBER: NCT05172206.
22 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Soft, skin-interfaced electronics enable cannula-free wireless monitoring of sleep respiration.
Sleep-related breathing disorders are prevalent yet frequently underdiagnosed, in part due to limitations of conventional respiratory monitoring technologies. Standard nasal cannulas introduce airflow resistance, discomfort, and poor long-term adherence, constraining at-home and longitudinal assessment. Here, we report a soft, skin-interfaced nasal patch that enables cannula-free, wireless monitoring of respiratory activity during sleep. The device is constructed from ultrathin, elastomeric materials that conform to the nasal surface, coupling respiratory-induced tissue deformation to a strain-sensing element. The mechanics of the skin-device interface and the elastomeric response govern the sensitivity and linearity of signal transduction, enabling quantitative capture of breathing dynamics. An integrated wireless platform transmits deformation signals directly to mobile devices, eliminating the need for external tubing or tethered modules. Modular fabrication permits replacement of the strain sensor and skin-contact interface without compromising mechanical performance. Mechanical characterization under physiologically relevant deformation demonstrates high repeatability and low hysteresis, while in vivo studies confirm that the patch accurately reproduces respiratory waveforms and correlates closely with gold-standard nasal cannula measurements. By integrating soft materials mechanics, wearable strain sensing, and wireless electronics, this system provides a minimally obtrusive platform for continuous respiratory monitoring. The class of technologies presented in this work establishes design principles for skin-interfaced devices, in which elastomeric mechanics, strain transduction, and wireless integration combine to enable quantitative, unobtrusive physiological monitoring in clinical and home environments.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Respiratory Syncytial Virus Vaccination in Solid Organ Transplant Recipients: Interim Findings From a Phase 3 Trial of mRNA-1345
BACKGROUND: Solid organ transplant (SOT) recipients are at increased risk for severe respiratory syncytial virus (RSV) disease due to chronic immunosuppression. METHODS: In this ongoing, open-label, phase 3 trial, adults ≥18 years with a history of liver, kidney, or lung transplant received 2 doses of mRNA-1345 RSV vaccine (50 µg) 56 days apart. Primary endpoints were tolerability, safety, and RSV-A/RSV-B neutralizing antibody (nAb) responses on Day 85; secondary endpoints included immunogenicity on Days 29 and 181. Cell-mediated immunity was an exploratory endpoint assessed in a subset of participants. RESULTS: 146/150 participants (median: age, 57 years; time since transplant, 4.7 years) received both doses. Reactogenicity was mild to moderate and transient. No vaccine-related discontinuations, deaths, adverse events of special interest (AESIs), or events of transplant rejection were reported within 28 days after any dose. One dose was immunogenic across all SOT types, with 4.9- and 3.4-fold increases in RSV-A/RSV-B nAb GMTs by Day 29, respectively. A second dose resulted in modest additional increases over baseline (RSV-A, 7.1-fold; RSV-B, 5.2-fold). Added benefit of the second dose was more apparent in participants with kidney and lung transplant, <2 years post-transplant, and on mycophenolate. Responses remained above baseline through Day 181. Polyfunctional CD4⁺ T-cell responses were robust and sustained; CD8⁺ responses were also observed. CONCLUSIONS: mRNA-1345 was well tolerated and immunogenic in SOT recipients. A single dose induced nAb responses across subgroups, with potential additional benefit from a second dose in specific groups. Durable antibody and cellular responses support mRNA-1345 as a preventive strategy for RSV in this vulnerable population. CLINICAL TRIALS REGISTRATION: NCT06067230.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Mycobacterium tuberculosis Transmission in Healthcare Settings: A Systematic Review of Frequency, Risk Factors, and Exposure Conditions
BACKGROUND: Uncertainty regarding factors that govern Mycobacterium tuberculosis transmission in healthcare settings leads to broad and variable exposure definitions and low-yield contact investigations. We systematically reviewed data on M. tuberculosis transmission in healthcare to inform risk stratification and exposure definitions. METHODS: We searched MEDLINE, Embase, CINAHL, and Cochrane databases from inception through December 2024 for studies describing M. tuberculosis transmission from adult patients to healthcare personnel (HCP) or patients. We tabulated data on transmission frequency, circumstances, and risk factors. RESULTS: Of 6695 studies screened, 86 met inclusion criteria, encompassing 1083 source patients, 35 698 exposed individuals, and 2517 secondary cases (7.1% overall transmission risk, 3.8% among studies published since the US Centers for Disease Control and Prevention last updated healthcare ventilation standards in 2005). Transmission was associated with prolonged contact between HCP and source patients (median duration 24 hours, interquartile range [IQR] 24-25 hours) and between patients in shared rooms (median duration 24 hours, IQR 12-24 hours), clinical care without N95 respirator (1232 transmissions/10 149 exposures that included data on face covering use, 12.1%), frequent direct care (185 transmissions/410 exposures, 45.1%), and lack of negative pressure ventilation (1906 transmissions/5670 exposures, 33.6%). Only 8/116 (6.9%) transmissions with documented exposure durations were associated with <8 hours of cumulative exposure and all in poorly ventilated spaces. CONCLUSIONS: Mycobacterium tuberculosis transmission in healthcare is uncommon, particularly in spaces that meet current healthcare ventilation standards. Risk factors for transmission include prolonged exposure, close contact, lack of respirators, and poor ventilation. Defining exposures as >8 hours without a respirator should identify most transmissions and could improve the efficiency of contact investigations.
22 May 2026
Read appraisal →European journal of pediatrics
The effect of virtual reality glasses used during inhaler treatment on anxiety, fear and vital signs in children: a randomized controlled trial
UNLABELLED: This study aimed to determine the effect of virtual reality (VR) glasses used during inhaler treatment on anxiety, fear and vital signs in children. The study had an experimental, randomized controlled design. Participants were assigned to groups using a lottery method based on simple randomization. The random sequence was generated by physically mixing up 30 red (experimental) and 30 white (control) cards. All the data were collected and finalized by two trained researchers (Yasemin Özyer Güvener, a nurse researcher, and Şeyma Şimşirgil Kara, a pediatrician). Participants were recruited using a convenience sampling method from among individuals who met the inclusion criteria. In terms of baseline measurements, the experimental group had significantly higher mean pulse rate, fear scores, and anxiety scores (130.23 ± 14.64, 2.77 ± 1.25, and 4.60 ± 2.04, respectively) compared to the control group (118.83 ± 17.10, 2.07 ± 1.26, and 2.80 ± 2.07, respectively) (p = 0.007, p = 0.035, and p = 0.001, respectively). For post-intervention measurements, the experimental group had significantly lower mean fear and anxiety scores (0.443 ± 0.16 and 0.703 ± 0.22, respectively) compared to the control group (2.39 ± 0.16 and 3.197 ± 0.22, respectively) (p < 0.001 and p = 0.001, respectively). CONCLUSION: It was determined that VR glasses reduced anxiety and fear, increased oxygen saturation, and reduced respiratory and pulse rates. They can thus be used as an alternative method to reduce fear and anxiety levels and balance vital signs during inhalation therapy in children. WHAT IS KNOWN: • Virtual reality (VR) is an effective non-pharmacological method for reducing pain, anxiety, and fear in pediatric patients during various medical procedures. • VR applications create a sense of presence in a virtual world, limiting the impact of anxiety and fear in clinical settings. WHAT IS NEW: • Using VR glasses during the administration of inhaled medication significantly reduces anxiety and fear levels in children compared to traditional methods. • While evidence for VR use during inhalation therapy was previously limited, this study demonstrates its effectiveness as a promising alternative to improve treatment compliance and physiological responses in pediatric patients.
21 May 2026
Read appraisal →Scientific reports
Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.
Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and health care systems. Both disorders affect voice production through different physiological mechanisms, yet neither condition has a widely adopted objective biomarker for routine clinical use. Voice analysis has emerged as a non-invasive digital biomarker candidate, but existing studies have largely focused on binary classification within a single disorder or language. This study aimed to evaluate whether an unified multiclass machine learning (ML) framework applied to sustained vowel "a" phonation can discriminate between PD, COPD, and healthy controls (HC) across linguistically distinct cohorts. Sustained vowel recordings were analyzed from Swedish speaking individuals with COPD and HC, and English-speaking individuals with PD and HC, collected under comparable mobile recording conditions. Acoustic features included baseline voice measures and Mel Frequency Cepstral Coefficients. A soft voting ML framework integrating support vector machine, random forest, CatBoost, and light gradient boosting classifiers was trained using nested cross validation with hyperparameter optimization. Data were partitioned at the participant level into a development cohort and an independent test cohort. Model performance was evaluated using accuracy, macro averaged precision, recall, F1 score, receiver operating characteristic analysis, and confusion matrices. Model interpretability was assessed using Shapley additive explanations and vowel space analysis. The final soft voting classifier achieved robust multiclass discrimination on the participant disjoint independent test set, with an overall accuracy of 0.842 and a macro averaged F1 score of 0.839. Classification performance differed across groups, with the highest performance observed for PD, intermediate performance for HC, and lower performance for COPD. Misclassifications occurred primarily between HC and COPD, while confusion between PD and COPD was minimal. Feature attribution analysis revealed class dependent relevance patterns, and vowel space analysis demonstrated subtle but consistent group level differences. These findings demonstrate the feasibility of using an explainable soft voting machine learning framework applied to sustained vowel phonation to distinguish between neurologically and respiratory driven voice impairments across linguistic contexts. The study supports voice as a promising digital biomarker modality for multiclass clinical discrimination using mobile recordings.
21 May 2026
Read appraisal →Biochemistry
Antimycobacterial Peptides: From Natural Product Discovery to AI Guided Design
Mycobacterial pathogens remain major global health threats, exacerbated by both rapid acquisition of antibiotic resistance and the formidable drug diffusion barrier presented by the rigid mycomembrane. These challenges have renewed interest in antimycobacterial peptides (AMyPs), a diverse class of short amphiphilic sequences capable of rapidly killing both drug-sensitive and drug-resistant mycobacteria. Beyond their intrinsic potency, AMyPs can synergize with existing antibiotics and exhibit markedly slower resistance development relative to conventional small molecules. In this review, we synthesize recent advances spanning natural bioprospecting, mechanism-guided rational design, and chemical optimization strategies that have yielded increasingly potent and selective AMyP candidates. We further highlight the rapid emergence of artificial intelligence-driven discovery platforms, which leverage machine-learning models trained on curated, mycobacteria-specific data sets to predict and refine novel AMyPs with growing accuracy. Together, these technologic, biologic, and computational advances outline a rapidly expanding landscape for AMyP-based therapeutic development and establish a foundation for next-generation antimycobacterial drug design.
21 May 2026
Read appraisal →BMJ open
Evaluation of a deep learning model applied to chest X-rays of patients with suspected pneumonia presenting to the emergency department designed to predict admission risk: a retrospective training and prospective non-intervention validation.
OBJECTIVES: To develop, train and test a deep learning model Image-based PROgnostication applied to Chest X Rays (IPRO-X) tool that predicts the inpatient (IP) admission risk in patients with suspected pneumonia presenting to the emergency department (ED). DESIGN: The study consists of a retrospective (training) and prospective non-interventional shadow deployment (validation) of a deep learning model. SETTING: Three-hospital tertiary care system with two EDs. PARTICIPANTS: Consecutive adult patients (18 years old or older) who presented to the ED from December 2022 to February 2023 with a chief clinical complaint potentially related to pneumonia were reviewed for eligibility (n=5567), with a final number of 3677 included in the validation study. PRIMARY AND SECONDARY OUTCOME MEASURES: IPRO-X was developed using standard two-dimensional convolutional neural network InceptionNet architecture and processes chest radiographs (CXRs) acquired at admission generating a continuous value from 0 (IP-negative) to 1 (IP-positive). We examined IPRO-X's ability to predict IP admission using accuracy, area under the curve (AUC), receiver operating characteristic, sensitivity, specificity, positive predictive value and negative predictive value. IPRO-X scores were compared against observed outcomes (admission and discharge) to determine clinical utility in an ED setting. Four thresholds were defined from the retrospective phase: Youden optimal, highest specificity and thresholds equivalent to 30-day mortality rates of Pneumonia Severity Index (PSI) Risk class IV and Confusion, Respiratory Rate, Blood Pressure and Age 65 or older Score (CRB-65) scores 1 and 2. Performance was also analysed per group of chief complaints. RESULTS: In the validation set, 3677 patients (1777 (48%) female, median age 56 years (min 18, max 99)) were included. IPRO-X predicted IP admissions with an AUC of 0.795, and in the pneumonia-specific chief complaints, AUC increased to 0.828, whereas in non-related pneumonia chief complaints, the AUC decreased to 0.755. IPRO-X score was significantly higher in admitted patients compared with discharged patients (p<0.001). The accuracy of IPRO-X when using the PSI-anchored threshold was 0.751 and with the CRB-anchored threshold was 0.729, with a specificity of 0.803 and 0.968, respectively. CONCLUSIONS: IPRO-X applied to CXR of patients with signs and symptoms commonly related to pneumonia in the ED can accurately predict IP admission.
21 May 2026
Read appraisal →The British journal of dermatology
Burden and risk of asthma and rhinitis in people with atopic dermatitis: global estimates from a hierarchical Bayesian model
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease often associated with asthma and rhinitis. However, epidemiological evidence on these comorbidities remains fragmented and is largely limited to high-income countries. OBJECTIVES: To provide comprehensive global, regional and country-level estimates of the prevalence and odds ratios (ORs) of asthma, rhinitis and their subtypes among individuals with AD. METHODS: We systematically reviewed 278 studies retrieved from PubMed, Embase, Web of Science and CNKI (China National Knowledge Infrastructure). Standardized prevalence estimates and ORs were derived using a Bayesian hierarchical linear mixed-effects model. RESULTS: Among individuals with AD, the global prevalence estimate of asthma, rhinitis, allergic rhinitis, rhinoconjunctivitis, and coexisting asthma and rhinitis was 20.1% (ranging from 2.5% in Israel to 59.4% in India), 45.1% (ranging from 7.8% in Israel to 82.7% in India), 41.4% (ranging from 6.8% in Israel to 80.3% in India), 30.7% (ranging from 4.3% in Israel to 71.9% in India) and 10.4% (ranging from 1.1% in Israel to 40.8% in India), respectively. Estimates were higher when restricted to patients with AD diagnosed by dermatologists or physicians. The highest comorbid prevalence rates were found for India, Cuba, Finland and Puerto Rico. Compared with healthy control individuals, AD was associated with higher odds of asthma (OR 2.73; ranging from 1.59 in China to 6.64 in Ethiopia), rhinitis (OR 2.98; ranging from 1.73 in China to 7.23 in Ethiopia), allergic rhinitis (OR 2.42; ranging from 1.41 in China to 5.89 in Ethiopia), rhinoconjunctivitis (OR 4.21; ranging from 2.45 in China to 10.19 in Ethiopia), and coexisting asthma and rhinitis (OR 2.42; ranging from 1.41 in China to 5.89 in Ethiopia). CONCLUSIONS: This study provides the first globally standardized estimates of respiratory comorbidities in AD, and can inform clinical practice and public health policy. Atopic dermatitis is a common long-term inflammatory skin disease. It is also called eczema. It affects more than 200 million children and adults worldwide. People with the disease often have other conditions, particularly asthma and rhinitis. Asthma affects breathing and rhinitis causes sneezing or congestion. Most studies on these conditions have been in wealthy countries. They have also used a range of different research methods. This makes it difficult to compare and understand the global situation. In this study, researchers in China and the USA analysed data from 278 studies from around the world. We estimated how common asthma and rhinitis are in people with eczema in different regions and countries. We found that about 1 in 5 people with eczema have asthma and almost half have rhinitis. We also found that people with eczema are 2 to 4 times more likely to have these conditions than people without eczema. Some countries showed particularly high rates of these conditions. Our findings suggest that asthma and rhinitis are common problems for people with eczema. This is true no matter where they live. The information from this study may help doctors recognize and manage these conditions in people with eczema. These findings could also support future research to improve disease prevention and patient care.
20 May 2026
Read appraisal →Physiological measurement
Perioperative hypoxaemia early warning based on high-frequency waveform fusion and deep learning
Objective.The accurate prediction of impending intraoperative hypoxaemia events is paramount for patient safety. Current models relying on structural parameters exhibit limitations, including low sampling frequency, weak temporal coherence, and high data attrition rates. Consequently, long-term sequence, high-frequency waveform data are utilised in this study to construct a deep learning-based hypoxaemia prediction model, by which conventional shortcomings are addressed whilst predictive accuracy and robustness are enhanced.Approach.Respiratory, blood oxygenation, and electrocardiogram waveforms were extracted from the VitalDB database. Two processing methods were employed: extraction of waveform-derived variables to construct numerical datasets; and utilisation of the Gramian Angular Summation Field (GASF) and the four-quadrant matrix method to construct image-based datasets. Through multiple dataset construction methods, five datasets were constructed. An LSTM algorithm was employed, using a 5 min learning window and prediction windows ranging from 1 to 13 min, to conduct a comprehensive performance evaluation.Main results.Optimal performance was achieved by all models within a one-minute prediction window. Highly competitive accuracy (0.932), AUC (0.953), and stability (maximum AUC variance 0.067) were demonstrated by the 'Multimodal image model', whilst the poorest performance was exhibited by the 'structured only model'. Moreover, compared to singlemodal models, multimodal models incorporating structured parameters yield only marginal improvements. Consequently, singlemodal data derived solely from raw waveforms are suggested as sufficient for prediction requirements, by which greater application potential is held due to enhanced computational efficiency.Significance.This study systematically demonstrated the significant advantages of high-frequency waveform data. Results indicate that waveform processing methods based on the GASF and the four-quadrant matrix approach can effectively enhance model stability and performance. Furthermore, the singlemodal model relying solely on waveform data, excluding structured parameters, can meet clinical prediction requirements while reducing computational costs. This study provides novel data structures and methodologies for perioperative hypoxaemia prediction.
20 May 2026
Read appraisal →Cancer causes & control : CCC
Prediction models for early detection and diagnosis of lung cancer in people who have never smoked: a systematic review and critical appraisal
BACKGROUND: Never-smokers can develop lung cancer, but this possibility is often overlooked by patients and physicians. We conducted a systematic review to identify existing prediction models that could be implemented in primary care or at a population level to facilitate early detection and diagnosis of lung cancer in never-smokers. METHODS: This study was registered on PROSPERO (ref: CRD42023374471). We systematically searched literature on the Medline, Embase, PsycINFO, and CINAHL databases published before 22 January 2025, with additional hand searching. Primary care or population-level data from subjects including never-smokers had to be used for model derivation. Studies involving specialized tests (radiological or genetic) were excluded. We used CHARMS to guide data extraction and critical appraisal, the TRIPOD statement for model evaluation, and PROBAST for risk of bias assessment. RESULTS: Among 2,431 studies retrieved, 31 models were included. Eight models were developed exclusively for never-smokers, but none were at low risk of bias. Among 23 models derived from never- and ever-smokers, five were at low risk of bias. Two were diagnostic models with 1-2 years prediction horizons, and three were prognostic models with 2-10 years prediction horizons. Methodological issues from the included studies were identified, analyzed, and discussed. CONCLUSION: This systematic review critically appraises and summarizes key information from currently available prediction models for lung cancer in never-smokers. The findings can inform future research to improve care and services for this underserved population.
20 May 2026
Read appraisal →Scientific reports
Cytokines and immunologic checkpoint molecules in predicting success of allergen immunotherapy.
There are large variations in how individual patients respond to allergen immunotherapy (AIT) against grass and/or birch allergy. There are currently no reliable biomarkers to predict which patients are likely to benefit from the treatment. The purpose of this study was to examine the potential of cytokine and soluble immunologic checkpoint molecule (ICM) levels as biomarkers for AIT success. Blood samples collected before starting AIT were analyzed for concentration of 92 different cytokines and 14 different ICMs. Traditional univariable statistical analysis was performed to evaluate differences between responders and non-responders. Furthermore, both unsupervised and supervised machine learning algorithms were used for multivariable analysis of differences between the responders and non-responders and to try to identify clusters within the subjects which could potentially be linked to endotypes of allergic rhinitis. Neither univariable nor multivariable analysis showed any significant correlations between AIT outcome and pre-treatment levels of cytokines or soluble ICM. In the cluster analysis, 4 clustering algorithms consistently grouped 48 of the 60 subjects into 3 distinct clusters. However, these clusters did not correlate with clinical characteristics, indicating that the clusters are unlikely to represent actual biological endotypes. The findings of this study did not provide evidence supporting the use of pre-treatment levels of cytokines and ICM as biomarkers for AIT outcome.
20 May 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Mortality risk and causes of death in exogenous Cushing's syndrome: a systematic review and meta-analysis
CONTEXT: Chronic oral glucocorticoids (GCs) are widely prescribed for multiple diseases. Although long-term GC exposure causes systemic toxicity, the magnitude, dose-response relationship, and causes of GC-associated mortality remain incompletely defined. OBJECTIVE: To quantify overall and cause-specific mortality associated with chronic oral GC exposure and evaluate dose-response relationships across underlying disease groups. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines (PROSPERO CRD42017067530). PubMed/MEDLINE, EMBASE, Cochrane Library, Web of Science, and CINAHL were searched from 1945 to March 2019. Eligible studies reported standardized mortality ratios (SMRs) or absolute deaths among adults receiving chronic oral GCs. Random-effects meta-analysis and mixed-effects meta-regression were used to synthesize mortality and dose-response associations across GC exposure metrics, exposure duration, and disease subgroups. RESULTS: One-hundred and sixteen studies encompassing 128 cohorts and 51 380 patients were included. Chronic GC exposure was associated with excess mortality (pooled SMR 1.87; 95%CI 1.32-2.61; I2 74.0%). The pooled proportion of death was 12.0% (95%CI 10-14%). Mortality risk was highest in inflammatory diseases (30.0%) and the vasculitides (18.0%). Higher cumulative dose (≥5 g prednisolone-equivalent), average daily dose >5 mg/day, and higher initial dose were each independently associated with increased mortality. Cardiovascular disease was the leading causes of death (25.6%), followed by malignancy (15.7%) and infection (13.4%). CONCLUSION: Chronic oral GC exposure was associated with increased mortality across disease groups, with higher cumulative and early-treatment doses correlating with higher risk. However, causal attribution remains uncertain due to confounding by indication, limited disease-severity data, and exposure misclassification. These findings support GC stewardship and targeted cardiovascular and infection risk mitigation, rather than indiscriminate dose reduction.
19 May 2026
Read appraisal →Environmental monitoring and assessment
Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology
Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. However, this is often time-consuming and labour-intensive. Automation can ensure up-to-date evidence; reduce retrieval, screening time, and costs; and make information accessible to non-academic stakeholders. This study investigated whether a machine learning approach can perform any stages of a traditional scoping review on the health impacts, policies, and interventions related to air pollution due to domestic waste burning. A traditional approach was conducted in parallel with machine learning methods enabling a comparison of the efficiency and quality of the partially automated approach against the manual review. Findings from the two approaches were compared and the final set of included articles were considered for (1) reported impacts of waste burning on health outcomes and (2) recommendations on solutions and interventions to prevent/reduce adverse effects on health from waste burning. We found a range of health impacts associated with waste burning, including low birth weight, hypertensive disorders of pregnancy, adverse respiratory outcomes like asthma and wheeze, cancer risk, and mortality. Few studies proposed solutions or evaluated the effectiveness of interventions. The ML approach showed a tendency towards false positives, which are preferable to false negatives (where relevant papers were excluded). Results showed that the model can conduct initial searches and decisions for the review. However, the articles included in the model should be screened manually for final acceptance. Therefore, we propose a hybrid approach be used until the automated model can be further refined.
19 May 2026
Read appraisal →Current allergy and asthma reports
Impact of IL-4/IL-13 Blockade with Dupilumab on the Microbiome in Type 2 Inflammatory Diseases: A Systematic Review
PURPOSE OF REVIEW: To systematically review current evidence on microbiota changes associated with dupilumab treatment across different anatomical sites in type 2 inflammatory diseases. RECENT FINDINGS: Fifteen studies were included, comprising two randomized trials and thirteen observational studies, mostly in atopic dermatitis, with fewer data in chronic rhinosinusitis with nasal polyps and NSAID-exacerbated respiratory disease. The skin was the most frequently investigated site, followed by the sinonasal tract and gut. Across skin studies, dupilumab was consistently associated with reduced Staphylococcus aureus, increased microbial diversity, and enrichment of commensal taxa. Sinonasal studies suggested shifts toward more eubiotic microbial communities. Gut evidence was limited, although one study suggested modulation of tryptophan metabolism-related pathways. Dupilumab appears to exert compartment-specific and disease-dependent effects on the microbiome. The strongest evidence concerns the skin and sinonasal compartments, whereas gut microbiota changes remain poorly defined. Further prospective studies are needed to assess microbiota signatures as potential biomarkers of response.
18 May 2026
Read appraisal →Environmental research
Association between air pollution and chronic cough: Evidence from a systematic review and meta-analysis of observational studies
BACKGROUND: Chronic cough is a common and debilitating respiratory symptom, yet its association with air pollution remains insufficiently understood. METHODS: We conducted a systematic review and meta-analysis of observational studies evaluating the association between air pollution and chronic cough. Searches of Medline, Embase, Scopus, and GeoBase were completed up to February 14, 2025. Studies were eligible if they reported quantitative associations between air pollution and exposures and chronic cough outcomes. Two reviewers independently screened studies, assessed quality, and extracted data. Random-effects models were used to estimate pooled odds ratios (ORs) with 95% confidence intervals. RESULTS: Of 1250 articles screened, 53 studies were reviewed, and eight were included in meta-analysis. Pooled ORs per 10 μg/m3 increase in exposure were 1.10 (95% CI: 0.99-1.22) for PM2.5 and 1.04 (95% CI: 1.02-1.06) for NO2. Heterogeneity was low to moderate (I2: 18.9% for PM2.5; I2: 0.0% for NO2), and there was no evidence of publication bias (Egger's test p = 0.98 and 0.47, respectively). Stratified analyses revealed stronger associations in adults and males. Evidence on progression from chronic cough to more severe respiratory conditions was limited, highlighting a critical gap for evidence-informed clinical follow-up and public guidance to prevent progression. CONCLUSION: Our findings highlight that chronic cough is an overlooked consequence of air pollution exposure and emphasize the need for health care professionals to consider environmental factors when managing persistent cough. Strengthening air quality control and early detection strategies could help reduce the respiratory disease burden.
18 May 2026
Read appraisal →Science advances
Wireless, skin-interfaced multimodal sensing system for continuous psychophysiological monitoring-A wearable polygraph device
Accurate, continuous monitoring of psychophysiological states is central to understanding stress and autonomic dysfunction across diverse medical contexts. Current approaches such as polygraphy and polysomnography rely on cumbersome, wired sensors that limit real-world utility and burden patients, particularly vulnerable populations such as infants. Here, we introduce a wireless, skin-interfaced multimodal sensing system capable of simultaneously recording cardiac, respiratory, electrodermal, and thermal signals in a time-synchronized manner. Leveraging compact and soft designs, the technology enables unobtrusive monitoring across controlled, clinical, and naturalistic settings. Validation studies performed in parallel with gold standard systems demonstrate high fidelity in quantifying stress responses during polygraph interviews, cognitive load tasks, and cold pressor tests. In pediatric sleep studies, the data reliably identify arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures in infants with Down syndrome. Real-world deployment during emergency simulation training shows that multimodal stress signatures correlate inversely with performance, underscoring translational value in medical education. Machine learning analyses across all studies confirm that multimodal features outperform single-signal approaches in detecting stress and clinical events with high sensitivity and specificity. Collectively, these findings establish the technology as a next-generation wearable platform that bridges engineering innovation and clinical practice, offering mechanistic insight and diagnostic potential in stress medicine, sleep medicine, and beyond.
18 May 2026
Read appraisal →European journal of pediatrics
Prevalence of common respiratory viruses of infants with respiratory tract infections in European countries in the past decade: a systematic review and meta-analysis comparing between the pre-COVID-19, pandemic, and post-COVID-19 periods
UNLABELLED: The purpose of this study is. to determine the leading cause of respiratory tract infections over the decade; this review examined the proportions of airway viruses in infants with respiratory tract infections in Europe before, during, and after the COVID-19 pandemic. The protocol for this systematic review was registered in the PROSPERO database (CRD42024497097). Literature searches in PubMed, Embase, Scopus, and Web of Science (WoS) identified 20,453 studies reporting respiratory viral testing in Europe over the past decade (2012/13-2022/23). Eligible studies were English-language full-text or grey articles with low risk of bias and complete data, and reports that explicitly provided the number of positive airway virus cases (n, virus) and the total infant population (N). Risk of bias was assessed using the Hoy et al. scoring system. Pooled prevalence estimates (pooled proportion [95% CI]) were calculated for pre-, during-, and post-COVID-19 periods using proportional meta-analysis with the MetaProp package in R. Fifty studies before, ten studies during, and 18 studies after the COVID-19 pandemic were eligible for proportional meta-analyses of viral testing in infants. Before the pandemic, the predominant viruses in infants with respiratory infections were Respiratory Syncytial Virus (RSV) (0.48 [0.41-0.56]), Human rhinovirus (HRV) (0.24 [0.18-0.29]), and Influenza virus (IV) (0.08 [0.04-0.12]). Post-pandemic, the leading viruses were RSV (0.63 [0.51-0.75]), HRV (0.25 [0.11-0.40]), and IV (0.10 [0.00-0.20]) again. CONCLUSIONS: RSV and HRV remained the two most identified viruses in infants with respiratory infections, pre- and post-COVID-19 pandemic. Interestingly, both RSV and IV were suppressed during the pandemic. The trajectory dynamics of respiratory viruses were divergent, with resilient viruses (RSV, HRV, and IV) regaining dominance more rapidly than persistently suppressed viruses. WHAT IS KNOWN: • The COVID-19 pandemic induced an infection precautions policy, which reduced the spread of respiratory infectious diseases. • Respiratory Syncytial Virus (RSV) was estimated as one of the most common causes of respiratory infections in infants. Still, there are no reports summarising the geographical distributions of the various viral agents of infant respiratory diseases. WHAT IS NEW: • This meta-analysis shows that the prevalence of RSV and HRV infections in infants remains high in Europe following the COVID-19 pandemic and the associated precautionary policy. Furthermore, RSV proportionally experienced a marked reduction during COVID-19 pandemic (lockdown periods). However, the immunological debt may have led to a re-emergence in RSV positivity after the lockdown periods. • Our proportional meta-analysis indicates the heterogeneous post-pandemic recovery patterns of each respiratory virus commonly identified in infants, where HRV was shown to be resilient whilst RSV was rapidly rebound.
18 May 2026
Read appraisal →Computers in biology and medicine
A unified multimodal framework for chest X-ray retrieval and disease prediction for clinical decision support
Recent advances in medical imaging and natural language processing enable new opportunities for automated diagnostic support. Chest X-rays (CXRs) remain the most common imaging modality for screening pulmonary, cardiovascular, and systemic diseases; however, the growing volume of studies and free-text reports can overwhelm clinicians. We propose a unified multimodal retrieval and prediction framework that jointly leverages DICOM-format CXRs and radiology reports by projecting visual and textual features into a shared semantic space. Trained with contrastive and multi-label objectives, the system supports disease classification, case-based retrieval, and explainable AI. Experiments on the Open-I dataset demonstrate strong performance, achieving a Macro AUROC of 0.95 and Macro F1-score of 0.71 across 22 diagnostic categories. The retrieval module attains high ranking quality (MRR and nDCG >0.93) with sub-millisecond query latency. Quantitative explainability analysis further shows strong agreement between attention- and gradient-based attribution maps (Pearson ρ≈0.92), supporting trustworthy clinical decision-making.
17 May 2026
Read appraisal →Psychological medicine
Effectiveness of a one-year smoking cessation intervention for people with severe mental illness: results of the KISMET cluster-randomized controlled trial
BACKGROUND: This study evaluated the effectiveness of a one-year smoking cessation intervention for people with severe mental illness compared with treatment as usual (TAU) in outpatient mental healthcare. METHODS: The KISMET study is a pragmatic cluster-randomized controlled trial conducted in 21 outpatient mental healthcare teams in the Netherlands. Eleven teams delivered the KISMET intervention comprising cognitive-behavioral and peer support, combined with optional pharmacological reatment. Ten teams participated in the TAU condition. We collected data between October 2022 and July 2024. The primary outcome was self-reported smoking cessation at 12 months, verified through exhaled carbon monoxide levels below 10 parts per million. Secondary outcomes included depression and anxiety (HADS), severity of psychotic symptoms (PANSS-6), quality of life (SF-12), disease self-management (PAM-13), lipid profile, blood pressure, body mass index, glucose level, and physical fitness. Crude and adjusted linear and multivariable logistic regression and mixed model analyses were performed. RESULTS: Eighty-nine participants were included in the KISMET intervention and 44 in TAU. Smoking cessation rates were significantly higher in the KISMET group at 3 months (OR 12.1, 95% CI 1.4 to 103.7) and at 12 months (OR 4.2, 95% CI 1.0 to 17.2) but not at 6 months (OR 1.9, 95% CI 0.5 to 6.9). No significant differences between groups were found for secondary outcomes. Dropout rates were 58% in the intervention and 32% in the TAU group. CONCLUSIONS: The KISMET intervention shows potential without signs of physical or psychopathological complications. However, results must be interpreted with the high dropout rates in mind.
17 May 2026
Read appraisal →PloS one
D-dimer and lower limb ultrasound as prognostic factors for recurrent deep venous thrombosis and pulmonary embolism: A systematic review and meta-analysis
Venous thromboembolic disease is a chronic, recurrent condition. The optimal duration of anticoagulation therapy remains uncertain. We aim to evaluate D-dimer and lower limb ultrasonography as prognostic tools for the recurrence of venous thromboembolism. A search was conducted on May 28, 2022, in the Medline, Embase, and Cochrane databases. Inclusion criteria encompassed cohort studies, case-control studies, and clinical trials. Two reviewers independently screened all records and analyzed studies for inclusion/exclusion criteria, as well as risk of bias, using a structured framework (PROSPERO ID: CRD42022341082). The initial search yielded 4652 titles and abstracts. After removing 777 duplicates and reviewing 3875 titles and abstracts, 48 articles providing information on D-dimer and/or lower limb ultrasonography as prognostic factors were finally included. Very low-certainty evidence suggests that both residual vein thrombosis (RVT) at anticoagulation discontinuation and a positive post-treatment D-dimer are significantly associated with an increased risk of recurrent venous thromboembolism (VTE). For RVT, the pooled analysis showed a two-fold increase in risk (OR 2.00, 95% CI 1.02 to 3.94; I² = 85.7) while for positive D-dimer, the risk was similarly elevated (OR, 2.48; 95% CI, 1.85-3.33; I² = 56.0). In conclusion, very low-quality evidence suggests that abnormal D-dimer and RVT are associated with recurrent VTE; however, this association is inconsistent due to significant heterogeneity and wide prediction intervals. These biomarkers should be interpreted with caution as isolated predictors in clinical practice.
16 May 2026
Read appraisal →PloS one
Optimising supervised machine learning algorithms predicting cigarette cravings and lapses for a smoking cessation just-in-time adaptive intervention (JITAI).
This study aimed to optimise the balance between participant burden and algorithm performance for predicting high-risk moments in a smoking cessation just-in-time adaptive intervention (JITAI) by systematically varying ecological momentary assessment (EMA) prompt frequency, predictor count, and training data source. Thirty-seven participants completed 16 EMAs per day for the first 10 days of their smoking cessation attempt, reporting mood, context, behaviour, cravings, and smoking lapses. Random forest algorithms predicting lapses and cravings were evaluated in terms of F1-score and ROC-AUC via mixed effects models accounting for clustering within individuals. Performance across out-of-sample individuals ranged from excellent to poor but was, on average, modest. Lapse prediction outperformed craving prediction, particularly for ROC-AUC (Median F1-score: Lapses 0.436 [IQR 0.180-0.625], Cravings 0.400 [IQR 0.048-0.649]; Median ROC-AUC: Lapses 0.659 [IQR 0.514-0.809], Cravings 0.628 [IQR: 0.510-0.729]). A substantial proportion of configurations fell below commonly used minimum performance thresholds, particularly for F1-score. Reducing EMA frequency had outcome- and metric-dependent effects. Lapse F1-scores improved with fewer prompts (16 EMAs: 0.254 [IQR 0.081-0.500], 3 EMAs: 0.588 [IQR 0.353-0.667]), while ROC-AUC showed a slight, inconsistent decline (16 EMAs: 0.661 [IQR 0.520-876], 4 EMAs: 0.613 [IQR 0.494-0.786], 3 EMAs: 0.704 [IQR 0.567-0.809]). For cravings, both metrics declined with fewer prompts (F1-score: 16 EMAs: 0.470 [IQR 0.141-0.745]; 3 EMAs: 0.333 [IQR 0.000-0.600]; ROC-AUC: 16 EMAs 0.700 [IQR 0.582-0.811], 3 EMAs 0.544 [IQR 0.421-0.676]). Feature reduction had negligible impact on lapse prediction (F1-score: all features 0.435, selected features 0.441; ROC-AUC: all 0.660, selected 0.657), but slightly reduced craving performance (F1-score: all 0.410 [IQR 0.117-0.646], selected 0.400 [IQR 0.000-0.650]; ROC-AUC: all 0.632, selected 0.622). Including participant-specific data improved lapse F1-scores (None 0.286 [IQR 0.000-0.571], 30 pc 0.542 [IQR: 0.329-0.667]), but not ROC-AUC (None 0.655 [IQR: 0.512-0.786], 30 pc 0.694 [IQR 0.513-0.852]); and impaired craving ROC-AUC (None 0.650 [IQR: 0.544-0.734], 30 pc 0.614 [IQR 0.493-0.730]; F1-score: None 0.424 [IQR 0.143-0.649], 30 pc 0.400 [IQR 0.000-0.703]). Overall, EMA-based machine learning detected lapse risk but showed modest overall performance and substantial inter-individual variability. Algorithms using higher EMA density, larger predictor sets, and participant-specific training data did not consistently outperform over more parsimonious approaches. However, machine learning prediction alone is unlikely to be sufficient for real-world JITAI implementation and may be best combined with complementary rules-based approaches.
16 May 2026
Read appraisal →Thorax
Measurement properties of the sit-to-stand test in community-dwelling people with chronic obstructive pulmonary disease: a COSMIN systematic review
BACKGROUND: The sit-to-stand (STS) test can assess physical function in people with chronic obstructive pulmonary disease (COPD); however, there are multiple versions. No study has used current guidelines to assess the measurement properties of the STS tests in people with COPD. METHODS: We conducted a systematic review using current COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines. Full text peer-reviewed publications were included if they assessed the measurement properties (validity, reliability and/or responsiveness) of at least one STS test among community-dwelling people with COPD. We searched six databases and imported results into Covidence where title/abstract screening and full text selection was completed independently and in duplicate. Extraction was conducted independently and in duplicate using the COSMIN extraction file. We assessed study risk of bias (very good, adequate, doubtful or inadequate), measurement property quality (sufficient, indeterminate or insufficient) and overall certainty of evidence (high, moderate, low or very low) using the COSMIN recommended tools. RESULTS: We assessed 2577 titles/abstracts and 102 full texts for inclusion; 30 publications met eligibility. Seven unique STS tests were located with the most common being the 1 min STS test (n=14, 39%), 5-repetition STS test (n=10, 28%) and the 30 s STS test (n=8, 22%). Where assessed, reliability was sufficient for the 1 min, the 5-repetition and the 30 s STS tests. Only the 1 min STS test had high-quality evidence of sufficient construct validity, while the 30 s STS test was the sole test with at least moderate quality evidence of sufficient responsiveness. CONCLUSIONS: The 1 min STS test has the most robust measurement properties for cross-sectional assessments while the 30 s STS test is more robust to assess change.
16 May 2026
Read appraisal →Thorax
Clinical impact of customised positive airway pressure (PAP) therapy interfaces (3DPiPPIn): a single site randomised controlled trial
INTRODUCTION: Positive airway pressure (PAP) therapy is the recognised treatment for sleep disordered breathing (SDB), delivered via a tight-fitting face mask (interface). Conventional interfaces do not consider facial geometries, often resulting in poor fit and ineffective therapy. Three-dimensional (3D) printing of customised interfaces may improve comfort and outcomes. OBJECTIVES: To evaluate the clinical impact of customised versus conventional oronasal interfaces in adults with obstrcutive sleep apnoea (OSA). The primary outcome was residual Apnoea Hypopnea Index (AHI) at 6 months; secondary outcomes included interface leak, therapy concordance and patient reported symptoms. METHODS: A randomised controlled trial with 160 adults naïve to PAP therapy and diagnosed with SDB (AHI ≥15 events/hour). Randomisation was minimised by age and ethnicity. Structured light facial scans (POP2, Revopoint, China) were used to produce 3D printed moulds (Fuse 30+, Formlabs, Massachusetts, USA) for silicone injected oronasal customised interface cushions.AHI was compared using quantile regression to account for the skewed distribution of the AHI data. Secondary outcomes were compared using logistic, quantile and linear regressions. RESULTS: 160 participants were recruited (intervention: 82, control: 78). Customised interfaces were associated with a 1.5 (events/hour) increase in AHI (p=0.059), higher interface leak (difference in medians 30.0 L/min, 95% CI 7.36 to 40.14, p<0.0001) and lower compliance (difference in compliance 0.78, 95% CI 0.05 to 1.54, p=0.04) at 6 months. CONCLUSIONS: This trial did not demonstrate customised oronasal interfaces were superior to conventional interfaces. Future research should focus on addressing design and manufacturing limitations before any potential advantages can be evaluated. TRIAL REGISTRATION NUMBER: ISRCTN74082423.
16 May 2026
Read appraisal →The Journal of infectious diseases
Cytomegalovirus DNAemia in Hospitalized Adults With SARS-CoV-2 Infection Requiring Supplemental Oxygen: Virologic and Clinical Characteristics and Association With Outcomes
BACKGROUND: Cytomegalovirus (CMV) reactivation occurs in the context of coronavirus disease 2019 (COVID-19); however, the viral kinetics, risk factors, and clinical outcomes are poorly defined. METHODS: We examined the association of CMV DNAemia with clinical outcomes among participants of a randomized trial of remdesivir with or without baricitinib (National Institute of Allergy and Infectious Diseases [NIAID], Adaptive COVID-19 Treatment Trial 2 [ACTT-2]). Plasma CMV DNAemia from CMV-seropositive participants with COVID-19 (NIAID ordinal scale [OS] 5, 6, or 7 at entry) were assessed longitudinally by quantitative polymerase chain reaction. Factors associated with CMV DNAemia, and clinical outcomes were analyzed by Cox regression and proportional odds models. RESULTS: Of 772 trial participants with available samples, 643 (83%) were CMV seropositive. Baseline CMV serostatus was not associated with COVID-19 outcomes. The cumulative incidence of CMV DNAemia among seropositive persons by day 28 was overall 11% (baseline OS 5, 6.3%; OS 6, 16.4%; OS 7, 24.7%), and was associated with older age, baseline OS, male sex, lymphopenia, and systemic corticosteroid use, while remdesivir and baricitinib did not affect risk. CMV DNAemia was associated with a lower probability of improvement by day 29 (adjusted hazard ratio, 0.3 [95% confidence interval, .17-.56]), with a more pronounced delay of recovery with higher CMV viral load. CMV DNAemia was also associated with higher severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load and death. CONCLUSIONS: In hospitalized adults with COVID-19 requiring oxygen, CMV viremia occurs within well-defined clinical risks and is independently associated with delayed recovery from illness, higher SARS-CoV-2 viral load, and increased mortality.
16 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Pollution to prevention: digital decision support for air pollution and exercise counselling in general practice.
BACKGROUND: Outdoor exercise supports physical, mental, and social wellbeing. However, exposure to air pollution during activity can increase cardiorespiratory risk. The Royal College of Physicians (A Breath of Fresh Air, 2025) urges clinicians to address environmental determinants of health, including air quality, during routine care, including annual checks. Patients increasingly use digital tools providing air-quality data and actionable behavioural recommendations, yet the quality, evidence base, and integration of these tools into clinical workflows remain unclear. AIM: To evaluate digital tools that help patients modify exercise behaviour based on air-pollution exposure to inform the development of a custom chatbot to support exercise counselling in primary care. METHOD: A scoping review of app libraries, grey literature, and peer-reviewed studies identified digital tools providing air-quality data with actionable exercise recommendations. Data extraction covered functionality, pollutants, validation, and quality using the Mobile App Rating Scale (MARS) and Behaviour Change Technique Taxonomy (BCTTv1). RESULTS: Seven tools were identified: IQAir, Plume Labs, BreezoMeter, EPA AirNow, PurpleAir, airTEXT, and AirTrack. MARS scores ranged from 2.9 to 4.3 (mean = 3.6/5.0), highest for functionality and aesthetics but lower for information accuracy and engagement. Behaviour change content was limited to general health information, and no tools demonstrated formal validation or clinical integration. CONCLUSION: Existing tools provide accessible air-quality data but lack individualised recommendations for exercise. Insights informed a custom chatbot embedding air-quality and exercise advice into GP cardiorespiratory annual reviews. Next steps include further development of the custom chatbot and pilot implementation in general practice to assess feasibility, clinician uptake, and patient outcomes.
16 May 2026
Read appraisal →Current allergy and asthma reports
Pharmacotherapeutic Medication Adherence in Patients With Allergic Rhinitis: A Systematic Review and Meta-Analysis
PURPOSE OF REVIEW: Medication adherence is essential for effective management of allergic rhinitis (AR), yet real-world adherence to guideline-recommended pharmacotherapies remains poorly characterized. This systematic review and meta-analysis aims to estimate pooled adherence proportions to prescribed intranasal corticosteroids and oral antihistamines in adults with AR, further stratified by adherence measurement method and geographic region. RECENT FINDINGS: Twelve studies encompassing 191,103 AR patients were included. Overall pooled adherence to both intranasal corticosteroids and oral antihistamines was 43%, with substantial heterogeneity across studies. Adherence estimates differed significantly by measurement method with self-reported adherence consistently exceeding pharmacy refill-based estimates for both medication classes. Significant geographic variation was also observed, with intranasal corticosteroid adherence ranging from 17% in North America to 61% in Asia, and oral antihistamine adherence ranging from 26% in North America to 48% in Northern and Western Europe. Peters' test did not indicate any significant publication bias and a Leave-One-Out (LOO) sensitivity analysis showed that no single study had a disproportionate effect on the overall effect size. Adherence to pharmacotherapy in AR is suboptimal, highly method-dependent, and varies significantly across geographic regions. These findings suggest that reported adherence rates are not only influenced by patients' behaviour, but also by the methods used to measure adherence and the broader healthcare environment. Integrating methodological rigour with geographic stratification provides more informative benchmarks for clinical practice and highlights the need for context-sensitive strategies to improve real-world AR management. Future research should prioritize standardized adherence measurement and prospective studies in underrepresented regions.
15 May 2026
Read appraisal →Annals of medicine
Impact on analgesia, diaphragmatic function, and recovery between erector spinae plane block versus superior trunk block in arthroscopic shoulder surgery: a randomized controlled trial
BACKGROUND: Effective analgesia and preservation of diaphragmatic function are key considerations in analgesia for shoulder surgery. The superior trunk block provides analgesia with reduced phrenic nerve involvement, while the erector spinae plane block offers minimal impact on diaphragm motion. This randomized controlled trial compared the analgesic efficacy, impact on diaphragmatic motion, and postoperative recovery between the two blocks. METHODS: Sixty patients undergoing arthroscopic shoulder surgery were randomized to receive either erector spinae plane block or superior trunk block. Primary outcomes were postoperative VAS and changes in diaphragmatic excursion. Secondary outcomes included Quality of Recovery-15 (QoR-15) scores, morphine-equivalent consumption, and the handgrip strength motor blockade. RESULTS: The superior trunk block resulted in significantly lower dynamic VAS at 1-h postoperatively (0.1 [0.0, 0.2] vs. 5.7 [4.0, 7.6]; p < 0.001) and reduced 24-h morphine consumption (7.8 [2.5, 15.0] mg vs. 12.7 [7.5, 17.3] mg; p = 0.038) compared to the erector spinae plane block. However, diaphragmatic excursion was better preserved in the erector spinae plane block group (8.37% ± 20.7% vs. -20.09% ± 22.2%; p < 0.001), with a lower incidence of partial hemidiaphragm paresis (3.3% vs. 46.7%; p < 0.001). At 24 h postoperatively, QoR-15 scores were higher in the superior trunk block group (p = 0.047), and no patient in either group developed handgrip motor blockade. CONCLUSIONS: Superior trunk block offers superior early postoperative analgesia and better overall recovery, while erector spinae plane block minimizes diaphragmatic impairment. However, the erector spinae plane block may represent an option only in carefully selected patients at high respiratory risk, acknowledging its significantly poorer early analgesic profile.
15 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Yield and safety of different biopsy types in interstitial lung disease: a systematic review and meta-analysis
BACKGROUND: Lung biopsy remains an important consideration in the management of interstitial lung disease (ILD). Transbronchial lung cryobiopsy (TBLC) offers an alternative to traditional surgical lung biopsy (SLB) and transbronchial forceps biopsy (TBB); however, up-to-date comparisons between these methods in ILD, particularly fibrotic ILD, remain limited. METHODS: A comprehensive literature search of Medline, Embase and Evidence-Based Medicine Reviews was performed for studies published between January 2000 and September 2025 to identify all citations reporting yield and complications of the three main lung biopsy methods for ILD. RESULTS: A total of 143 studies were included. Pooled multidisciplinary yield (following multidisciplinary discussion) in TBB, TBLC and SLB was 35.3% (95% CI 21.3-52.5), 84.3% (95% CI 81.7-86.5) and 88.6% (95% CI 83.4-92.3), respectively. For fibrotic ILD, the multidisciplinary yield was 12.5% (only one eligible study), 87.6% (95% CI 83.5-90.7) and 87.3% (95%CI 80.5-92.0), respectively. Meta-regression showed an increasing multidisciplinary yield of TBLC with more recent years of patient recruitment and an increasing percentage of fibrotic ILD in each study. The multidisciplinary yield of both TBLC and SLB was 85-90%, suggesting a biopsy "ceiling effect". All evaluated complications were more frequent in TBLC compared to TBB and some were more frequent in SLB compared to TBLC. Several complications were more frequent in fibrotic ILD compared to all ILD. CONCLUSIONS: The multidisciplinary yield of TBLC and SLB is equivalent, with both offering an acceptable safety profile. TBB has low yield, particularly in fibrotic ILD. These findings support using either TBLC or SLB as an initial diagnostic tool in fibrotic ILD, mainly based on patient-physician shared decision-making.
15 May 2026
Read appraisal →BMJ open
Developing and validating an electronic health record-embedded AI model for managing multimorbid hospitalisation risk in patients with chronic RESpiratory disease (AiRES): a study protocol
BACKGROUND: Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a high multimorbidity burden. However, existing risk assessment instruments prioritise physiological measures while overlooking systemic comorbidities. We aim to develop and validate an electronic health record (EHR)-embedded artificial intelligence (AI) model-AiRES (AI in patients with RESpiratory disease)-to predict the 30-day, 90-day and 180-day risks of all-cause and index-disease hospitalisations. This model represents a first step towards a clinical decision support tool for personalised multimorbidity management in patients with CRD. METHOD AND ANALYSIS: Patients aged ≥18 years with a validated case definition of asthma and COPD will be identified from Singapore health administrative data (2012-2020). Candidate predictors will include age, sex, ethnicity, housing type, and comorbidities, measured across multiple care settings as visit frequency, grouped at quarterly intervals in Year 1 and annually for Years 2 and 3 over a 3-year lookback window. We will predict 30-day, 90-day, and 180-day risks of (1) all-cause and (2) asthma/COPD-specific hospital admissions using up to five randomly selected index dates per individual. Three machine learning algorithms-logistic regression (LR) with Lasso regularisation, eXtreme Gradient Boosting, and Categorical Boosting-will be trained using 10-fold cross-validation (CV) with an ensemble feature selection strategy. The optimal model, selected based on performance and feature importance, will be benchmarked against two reference models: a full LR and a Zero-Inflated Negative Binomial regression with hospitalisation history as the sole predictor. Discrimination and calibration will be assessed using internal-external cluster-based and temporal CV. Clinical utility will be evaluated using decision curve analysis. ETHICS AND DISSEMINATION: This study obtained ethics approval from the National University of Singapore (NUS-IRB-2024-849). Results will be published in international peer-reviewed journals.
15 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Evolution of body image across treatment eras: a systematic review in young people and adults living with cystic fibrosis
OBJECTIVES: Advancements in treatments for cystic fibrosis (CF) have improved prognosis, but variant-specific therapies have also driven weight gain. Traditional challenges to maintain weight are now less prevalent, with new body image concerns potentially emerging. This review aims to synthesise quantitative and qualitative evidence on body image in adults and young people (aged ≥16 years) with CF. METHODS: Systematic search of MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health, the Allied and Complementary Medicine Database and the Cochrane Library (January 2011 to August 2025) using CF-related keywords including body image and physical appearance. RESULTS: Searches retrieved 29 eligible studies (26 quantitative, two qualitative, one mixed methods). The Cystic Fibrosis Questionnaire - Revised (CFQ-R) and Cystic Fibrosis Quality of Life Questionnaire (CFQoL) questionnaires assessed body image quantitatively (0-100). 15 studies reported CFQ-R body image scores (58±19 to 78±23) and five reported CFQoL body image scores (21±26 to 76). Pre-modulator era (≤2012) scores ranged from 64±27 to 70±27; initial rollout phase (2012-2019) scores ranged from 58±19 to 81 (post-ivacaftor); and in the widespread modulator era (2020 to present) were 63±32 to 67 (31). Gender effects were inconsistent. Higher body mass index, weight, and body fat were linked to better body image in some studies; psychological wellbeing and lower anxiety/depression consistently correlated with body image. Ivacaftor and life coaching improved body image, while elexacaftor/tezacaftor/ivacaftor, cognitive behavioural therapy, and self-management programmes showed no effect. Functional health was strongly linked to body image.Qualitative findings highlighted influences of gender, weight change, functional and mental health, social support, early eating experiences, and limited clinician-initiated body image discussions. CONCLUSION: This is the first systematic review of body image in CF since new drug regimens emerged. Emerging body image disturbance patterns highlight the need for clinical strategies to screen for and address body image issues in CF care.
15 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Exploring the role of cold air in airway hyperresponsiveness and asthma diagnostic testing: a systematic review and meta-analysis
BACKGROUND: Asthma is a heterogeneous disease, making uniform diagnosis challenging, resulting in both under- and overdiagnosis. We conducted a systematic review and meta-analysis to summarise the data on diagnostic accuracy of a cold air bronchial challenge test (CACh) in exercise-induced bronchoconstriction and asthma. METHODS: Databases (PubMed, Embase, Web of Science core collection, Cochrane Library and the Cumulative Index to Nursing and Allied Health Literature via EBSCO) were systematically screened for articles evaluating the use of CACh as a diagnostic tool in exercise-induced bronchoconstriction and asthma. A random-effects model was used to calculate the effect on the reduction in forced expiratory volume in 1 s (FEV1) by CACh in healthy controls compared to patients with asthma. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-Comparative. This study is registered with PROSPERO (www.crd.york.ac.uk/PROSPERO/ identifier number CRD42021290350). RESULTS: After screening and removal of duplicates, 39 studies were included. Aggregate sensitivity and specificity of a FEV1 decrease ≥10% after CACh were 0.808 and 0.959, respectively. The weighted mean difference in percentage FEV1 decrease after CACh for patients with asthma compared to healthy controls was -17.58. High heterogeneity (I2=99) was mainly explained by differences in reference tests to diagnose asthma. Application of the trim-and-fill method to account for publication bias imputed eight studies, resulting in a notable reduction in heterogeneity. The overall effect (a significant decrease in FEV1 in patients with asthma) remained statistically significant (-8.18, p<0.001). CONCLUSION: Our study shows the high sensitivity and specificity and possible clinical implications of a CACh in the diagnosis of asthma. Further research is needed to confirm this.
15 May 2026
Read appraisal →The New England journal of medicine
Ensitrelvir for Covid-19 Postexposure Prophylaxis in Household Contacts
BACKGROUND: Ensitrelvir, an oral inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like protease, is approved in Japan for the treatment of mild-to-moderate coronavirus disease 2019 (Covid-19). Previously, no antiviral agents were approved for postexposure prophylaxis in household contacts of patients with Covid-19. METHODS: In this double-blind, randomized, placebo-controlled trial, we randomly assigned persons who were SARS-CoV-2-negative on local diagnostic testing but were household contacts of a patient with Covid-19 (the index patient) to receive either ensitrelvir (375 mg on day 1 and 125 mg daily on days 2 through 5) or placebo within 72 hours after symptom onset in the index patient. The primary end point was Covid-19 (defined by a central laboratory-confirmed positive reverse-transcriptase-polymerase-chain-reaction assay and the presence of ≥1 of 14 prespecified Covid-19 symptoms lasting ≥48 hours) by day 10 in a household contact in the modified intention-to-treat population (all the participants who underwent randomization, had a central laboratory-confirmed negative RT-PCR test for SARS-CoV-2 at baseline, and received at least one dose of the trial drug or placebo). RESULTS: The modified intention-to-treat population included 1030 participants in the ensitrelvir group and 1011 in the placebo group. The mean age of the participants was 42.4 years; 71.1% had undergone randomization within 48 hours after symptom onset in the index patient, and 37.0% had at least one risk factor for severe Covid-19. The incidence of Covid-19 was lower in the ensitrelvir group than in the placebo group (2.9% vs. 9.0%; risk ratio, 0.33; 95% confidence interval [CI], 0.22 to 0.49; P<0.001). The incidence of adverse events during the trial was similar in the two groups (15.1% in the ensitrelvir group and 15.5% in the placebo group), as was the incidence of serious adverse events (0.2% in each group). No Covid-19-related hospitalizations or deaths were reported. CONCLUSIONS: Ensitrelvir administered to household contacts of a patient with Covid-19 within 72 hours after symptom onset in the index patient was effective in preventing Covid-19 in the contacts. (Funded by Shionogi; SCORPIO-PEP Japan Registry for Clinical Trials number, jRCT2031230124; ClinicalTrials.gov number, NCT05897541.).
15 May 2026
Read appraisal →PloS one
Evaluation of the diagnostic value of the Modified Evan's Blue Dye Test for assessing aspiration in tracheostomized critically ill patients: A systematic review and meta-analysis
Dysphagia and aspiration are common complications in tracheostomized critically ill patients. The Modified Evan's Blue Dye Test (MEBDT) is a non-invasive, bedside, adjunctive rule-in signal for aspiration when positive. However, its diagnostic accuracy compared to the gold standard Fiberoptic Endoscopic Evaluation of Swallowing (FEES) in these patients remains unclear. We conducted a systematic review and meta-analysis to evaluate the diagnostic accuracy of the MEBDT for the detection of aspiration in tracheostomized critically ill patients. Study protocol was prospectively registered on PROSPERO (CRD:42023479920). A systematic search was performed across three databases (PubMed, Cochrane Central, and Embase), followed by a systematic screening process against predetermined selection criteria and included studies that provided data on the sensitivity and specificity of MEBDT versus FEES. The risk of bias and the level of evidence certainty in the included papers were assessed by the QUADAS-2 tool and GRADE approach respectively. Six out of 2227 screened studies were included. We found that MEBDT had a high specificity 95.42% (95% CI [67.38%, 99.53%]) and positive predictive value 95% (95% CI [81, 100]). In conclusion, the MEBDT can serve as a bedside adjunctive tool that indicates a potential aspiration risk when the result is positive. However, if the result is negative, further diagnostic assessments, like FEES, are recommended for high-risk patients.
15 May 2026
Read appraisal →European journal of pediatrics
Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.
UNLABELLED: Smartphone-based lung auscultation, combined with artificial intelligence (AI), may offer a promising alternative for pediatric respiratory telemonitoring. The aim of our study was to evaluate the performance of an AI model on wheeze detection from pediatric respiratory sounds recorded via smartphone. An observational cross-sectional study was conducted at the Pediatric Department of a Portuguese tertiary hospital, including children aged 0-17 years (pre-school, school-aged, and adolescent), with or without respiratory disease. Respiratory sounds were recorded at four locations using smartphone microphones and independently classified by at least 2 blinded annotators for quality and presence of wheezes. A hybrid convolutional-recurrent neural network (CNN + LSTM) trained on public electronic stethoscope databases was used to detect wheezes. AI model performance was assessed using positive predictive value, sensitivity, specificity, accuracy, and F1-score. A total of 217 children (59.9% male; median 10 [Q1-Q3 4.5-13] years) were included. From 2020 respiratory sound recordings, 1500 (74.3%) met quality criteria. Manual annotation identified 271 recordings with wheezes, whereas AI detected 217, most frequently in pre-school children (64.5% AI; 53.9% manual). The model achieved an overall accuracy of 87% (95% CI 86-89) and an F1-score of 61% (95% CI 56-66). The best accuracy was found in adolescents (92%, 95% CI 90-94) and the best global F1-score in pre-school children recordings (64%, 95% CI 58-71). CONCLUSION: This study supports the feasibility of AI-assisted analysis of smartphone-recorded pediatric respiratory sounds in real-world settings. Future multicenter studies with larger datasets and model fine-tuning using smartphone recordings are expected to improve AI model performance and generalizability. WHAT IS KNOWN: • Continuous monitoring of pediatric respiratory diseases is essential to improve outcomes and reduce healthcare and family burden. • Smartphone lung auscultation is feasible in children but AI wheeze detection models have only been applied to recordings from electronic stethoscopes. WHAT IS NEW: • First study applying an AI model to detect wheezes in smartphone-recorded lung sounds. • Quality recordings were obtained across all pediatric age groups and the AI model performance showed promising results, comparable to studies using electronic stethoscopes.
14 May 2026
Read appraisal →JMIR medical informatics
Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study
BACKGROUND: The widespread adoption of electronic health records has resulted in the generation of large volumes of clinical notes. Learning algorithms and large language models can be trained on these resources, but they are susceptible to noise-irrelevant or noninformative data. This sensitivity can lead to significant challenges, including performance degradation and the generation of inaccurate predictions or "hallucinations." This study addresses a critical challenge in clinical informatics: efficiently filtering millions of documents for relevance before advanced language model processing, particularly in resource-constrained environments. OBJECTIVE: We present a novel framework for determining document relevance in clinical settings using a chronic obstructive pulmonary disease (COPD) dataset. METHODS: We developed a novel framework using weak supervision and domain-expert heuristics to generate "silver standard" labels for training data and gold standard expert-annotated labels, creating 2 datasets to optimize the model during the development phase and subsequent testing phase. Various text representation techniques (bag of words, term frequency-inverse document frequency, lightweight document embeddings, compression-based features, and Unified Medical Language System concept extraction) were evaluated. These representations were used to train random forest, extreme gradient boosting, and k-nearest neighbor classifiers. Models were optimized on a small expert-annotated dataset and evaluated on a held-out test set. RESULTS: The combination of lightweight document embedding with a random forest classifier demonstrated the best performance, achieving a precision of 0.73, recall of 0.86, and F1-score of 0.80 (95% CI 0.76-0.87) for identifying relevant COPD documents. This significantly outperformed baseline heuristics (precision=0.70; recall=0.38; F1-score=0.50, 95% CI 0.43-0.56) and other tested methods. CONCLUSIONS: Our study presents a novel framework for identifying COPD-relevant clinical documents using lightweight embedding and machine learning. This approach effectively filters pertinent documents, enhancing information retrieval precision. The framework's scalability and minimal annotation needs make it promising for diverse health care applications, potentially optimizing clinical outcomes through efficient document selection for data-driven decision support systems.
14 May 2026
Read appraisal →Journal of computer-aided molecular design
Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights
Tuberculosis (TB) remains a major global health challenge driven by persistent Mycobacterium tuberculosis infection and increasing drug resistance. Phytochemicals represent a structurally diverse and underexplored chemical space for anti-TB drug discovery, yet systematic prioritization strategies integrating machine learning and structure-based validation are limited. A curated phenotypic anti-TB dataset of 425,180 compounds was used to train ensemble ExtraTrees models based on ECFP4 fingerprints and physicochemical descriptors. The models achieved strong predictive performance (ROC-AUC up to 0.983; MCC up to 0.871). SHAP analysis enabled mechanistic interpretation by identifying the key molecular descriptors and fingerprint features driving anti-TB activity predictions. The validated ensemble was applied to screen 4707 phytochemicals, yielding 3209 predicted actives, of which 778 satisfied applicability domain criteria. High-confidence candidates were subsequently evaluated by molecular docking against twelve structurally validated essential M. tuberculosis targets spanning cell wall biosynthesis, energy metabolism, nucleotide synthesis, and cofactor pathways. Docking analysis identified 486 phytochemicals with favorable predicted binding affinities, including 193 compounds exhibiting multi-target engagement. Several top-ranked candidates reproduced canonical interaction patterns of co-crystallized inhibitors, supporting mechanistic plausibility. This integrated chemoinformatics and structure-based framework enables robust prioritization of phytochemicals with biologically meaningful and multi-target antitubercular potential. The study provides a computationally grounded strategy for accelerating lead identification against drug-resistant TB.
13 May 2026
Read appraisal →European journal of pediatrics
Machine learning-based integration of respiratory oscillometry and spirometry for predicting clinical outcomes in spinal muscular atrophy
UNLABELLED: Assessing respiratory function in spinal muscular atrophy (SMA) is challenging due to the effort-dependent nature of traditional spirometry. While respiratory oscillometry offers a passive alternative, its clinical utility in predicting clinical status remains underexplored. This study evaluates the efficacy of integrating spirometric and respiratory oscillometric indices through machine learning (ML) to predict ambulatory status and the requirement for bilevel positive airway pressure (BiPAP) in SMA patients. We retrospectively analyzed forty-five patients with a genetically confirmed diagnosis of SMA. To address data imbalance, we utilized the SMOTETomek technique and validated Random Forest models through leave-one-out cross-validation. SHAP (SHapley Additive exPlanations) analysis was used to interpret the contribution of individual physiological markers. In univariate logistic regression, forced vital capacity (FVC), peak expiratory flow (PEF), and forced expiratory flow at 75% of vital capacity (FEF75) were significant predictors for walking support. FEF25, FEF50, FEF75, and FEF 25-75 were associated with the requirement for BiPAP use. None of the respiratory oscillometric parameters reached statistical significance in regression models. However, ML models achieved high predictive accuracy, with SMOTETomek improving BiPAP requirement recall from 0.00 to 1.00. SHAP analysis revealed that respiratory oscillometry gained significant predictive weight that traditional models overlooked. R20Hz emerged as a key predictor for ambulatory status, while R5-20 Hz was identified as a decisive feature for predicting the BiPAP use. CONCLUSION: The integration of effort-independent respiratory oscillometry with traditional spirometry via ML has the potential to enhance risk stratification in SMA. WHAT IS KNOWN: • SMA requires lung function monitoring, but traditional spirometry is challenging due to the need for forceful expiratory maneuvers. • Respiratory oscillometry provides an effort-independent assessment of respiratory mechanics through tidal breathing. WHAT IS NEW: • Machine learning successfully integrates effort-independent respiratory oscillometry with spirometry to significantly enhance clinical predictions in SMA. • It identifies R20Hz and R5-20 Hz as critical physiological markers for predicting ambulatory status and the requirement for respiratory support, respectively.
13 May 2026
Read appraisal →Journal of medical systems
Feasibility, Adherence, Acceptance and Usability of a Multimodal Telemonitoring for Pediatric Post-COVID Syndrome: A Bicentric Pilot Study
Existing healthcare infrastructure struggles to meet the complex care required for pediatric Post-COVID Syndrome (pPCS). Telemonitoring offers potential to enhance care access, reduce patient burden, and ensure continuity. This study introduces and evaluates a novel, multimodal telemonitoring concept for pPCS with high translational potential for broader pediatric chronic and post-infectious conditions. Telemonitoring included a patient app, digital sensors (spirometer, smartwatch), Patient Reported Outcome Measures, chat/video consultations (VC), and a medical telemonitoring platform. Patients aged 12-17 years with diagnosed PCS were recruited from two pPCS outpatient university clinics in Bielefeld and Munich, Germany. Monitoring lasted three months. Evaluation focused on feasibility, adherence, acceptance, and usability, using monitoring data, the System Usability Scale (SUS), Technology Usage Inventory (TUI), and a custom survey completed by patients and parents. 30 patients (mean age: 15y ± 1.9; 57% female (17/30); mean Baseline Bell-Score: 36.4) and 30 parents participated. Adherence was high, with an average of 3.4 (smartwatch) to 4.6 (spirometry) measurements/week. Questionnaire response rate was 86% (411/480) and 97% (58/60) of VCs were conducted. SUS scores indicated very high usability (patients: 81.25/100; parents: 75.42/100). TUI results showed low skepticism, and high interest. Telemonitoring supported symptom management independent of in-person visits, despite sensor connectivity issues. This is the first study to demonstrate successful integration of telemonitoring in pPCS, with high adherence and positive feedback from all stakeholders supporting its potential. Despite occasional technical challenges and resource needs, this concept shows promise for broader hybrid telemonitoring care implementation in PCS and other post-infectious syndromes. TRIAL REGISTRATION: German Clinical Trials Register (DRKS), trial registration number: DRKS00029354. Registered 07 February 2023 - Retrospectively registered https://drks.de/search/en/trial/DRKS00029354/entails.
13 May 2026
Read appraisal →Physics in medicine and biology
A delta-based multi-omics model to predict adaptive radiotherapy benefit in patients with non-small cell lung cancer during carbon-ion radiotherapy
Objective.Patients with non-small cell lung cancer (NSCLC) treated with carbon-ion radiotherapy (CIRT) are potential candidates for adaptive radiotherapy (ART). However, ART is time- and resource-intensive. This study aimed to develop a decision-support tool to estimate ART demand before treatment.Approach.This retrospective study included 69 patients with NSCLC treated with CIRT. Baseline clinical characteristics, dose-volume histogram (DVH) parameters, and radiomic and dosiomic features from the plan-computed tomography (CT) (1-2 weeks before treatment) and confirm-CT (1-2 d before treatment) were collected, and relative change rates in features (ΔDVH, Δradiomics, and Δdosiomics) were calculated. Feature selection involved univariable testing, correlation analysis, least absolute shrinkage and selection operator regression, and stepwise regression with 1000 bootstrap resamples. A multivariable logistic regression model was constructed and validated using 200 iterations of five-fold cross-validation.Main results.ART was triggered in 32 (46.4%) patients. A five-feature delta-omic model (one ΔDVH, two Δradiomic, and two Δdosiomic features) achieved a validated median area under the curve of 0.897 (interquartile range, 0.833-0.950) with good calibration. At a high-sensitivity threshold (0.11), the model identified all ART candidates while potentially reducing the frequency of weekly-CT evaluations in 21.4% of patients; at the maximum-Youden threshold (0.41), the median accuracy reached 78.6%, with up to 50.0% classified as low ART demand.Significance.A delta-omic model predicting ART demand in patients with NSCLC receiving CIRT was developed. The model may help optimize resource allocation and streamline ART workflows, although prospective validation in larger cohorts is warranted before widespread clinical adoption.
13 May 2026
Read appraisal →Med (New York, N.Y.)
Evaluation of recombinant human prourokinase for acute pulmonary embolism: A phase 2, randomized clinical trial
BACKGROUND: Recombinant human prourokinase (rhPro-UK) is a novel plasminogen activator under investigation for acute pulmonary embolism (aPE). We aimed to explore the efficacy and safety of rhPro-UK vs. alteplase (recombinant tissue plasminogen activator [rt-PA]) in patients with aPE. METHODS: In this phase 2, randomized, single-blind, multicenter, active-controlled, randomized trial involved eighteen centers from university-affiliated tertiary hospitals across China. Patients aged 18-75 years with high or intermediate-to-high risk aPE were randomized to receive intravenous rhPro-UK (40 mg, n = 37; 50 mg, n = 35) or rt-PA (n = 35) and were followed for 7-30 days. The primary efficacy outcome was the change from baseline in systolic pulmonary artery pressure (sPAP) at 24 h post-treatment. Secondary outcomes include changes in right ventricular function parameters. Safety outcomes include all-cause mortality, recurrent PE, hemodynamic deterioration within 7 days, and bleeding events within 30 days. FINDINGS: All treatment groups showed a reduction in sPAP at 24 h (mean change: 40 mg rhPro-UK, -13.40 mmHg [95% confidence interval (CI): -24.10 to -2.71]; 50 mg rhPro-UK, -15.42 mmHg [95% CI: -25.93 to -4.91], and rt-PA: -16.02 mmHg [95% CI: -25.53 to -6.51]), with improvements sustained through 30 days. The incidence of non-major bleeding events was numerically lower in the rhPro-UK groups (40 mg: 63.9%; 50 mg: 55.6%) compared to the rt-PA group (82.9%; p = 0.04). Two deaths occurred, each in the 40 mg rhPro-UK group and rt-PA group. CONCLUSION: rhPro-UK tended to result in early hemodynamic improvement comparable to rt-PA and seemed to have a numerically lower risk of non-major bleeding events. The ClinicalTrials.gov identifier is NCT03108833. FUNDING: This work was funded by Tasly Biopharmaceuticals Co., Ltd.
12 May 2026
Read appraisal →Vaccine
Enhancing uptake of respiratory vaccinations in asthma and chronic obstructive pulmonary disease (COPD) patients: a systematic review.
INTRODUCTION: Despite influenza, pneumococcal and COVID vaccines being widely recommended for patients with chronic respiratory disease, vaccination rates in this cohort remain low. The aim of this systematic review is to identify interventions which are effective in increasing respiratory vaccination rates in adults with chronic obstructive pulmonary disease (COPD) or asthma. METHODS: The inclusion and exclusion criteria for the study can be found in the PROSPERO protocol (PROSPERO registration no: CRD42025588565). A search was run across four databases (MEDLINE, Embase, CENTRAL and ClinicalTrials.gov) in February 2025, which returned 2537 studies. Eleven studies were deemed to meet the study inclusion/exclusion criteria and these were narratively synthesised: four randomised clinical trials (RCTs), six longitudinal studies and one observational cohort study. Risk of bias was assessed using the Cochrane's Risk of Bias (RoB-V2) and ROBIN-I-V2 tools. Studies were categorised according to the COM-B model of behaviour change. RESULTS: All 11 studies consisted of COPD populations, with four studies also including asthma patients. Interventions focused on patient education, with/without involvement of a healthcare professional (HCP). Nine of the eleven studies showed a statistically significant improvement in influenza and/or pneumococcal vaccination rate with an intervention. No studies assessing COVID vaccine uptake in this population were suitable for inclusion. Most studies targeted patients' capability to get vaccinated through improving patients' and HCPs' knowledge. Fewer studies focused on social opportunity (e.g. support from other patients/HCPs) or automatic motivation (e.g. reminders). DISCUSSION: The published literature in this area is currently limited. Most studies are non-randomised and are at high-risk of bias, making meta-analysis not possible. Further research should assess the practicalities (physical opportunity) of vaccination, especially in low-income economies (where most respiratory patients reside) and standardising research methods to allow for future meta-analysis. OTHER: There is no funding for this review.
12 May 2026
Read appraisal →BMJ open respiratory research
Prevalence and determinants of drug-resistant pulmonary tuberculosis in the MENA region: a systematic review and meta-analysis
BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) is a constraining challenge for tuberculosis (TB) control worldwide. The Middle East and North African (MENA) region represents a significant part of the global MDR-TB burden. AIM: To estimate the pooled prevalence of MDR-TB and its determinants in the MENA region. METHODS: We searched for studies published in English and French on the subject up to 31 January 2026 on Web of Science, PubMed, Scopus and Cochrane, without time restriction. Original studies reporting data on the prevalence of MDR-TB in individuals living in the MENA region were selected. The meta-analysis was done using the random effects model considering the heterogeneity among the included studies and I 2 statistic was used to assess the heterogeneity. RESULTS: A total of 1239 articles were identified and 25 studies from 6 countries were included in this review. The prevalence of MDR-TB in the MENA region ranged from 0% (95% CI 0% to 4.1%) to 17.1% (95% CI 10.6% to 25.4%). The pooled prevalence was 3.54% (95% CI 2.18% to 5.72%) with a high heterogeneity, I2 =95.6%; 95% CI 94.4% to 96.5%. Previous exposition to TB treatment, HIV infection, smoking and the presence of comorbidities were the most reported associated factors. CONCLUSION: This review underscores the persistence of MDR-TB in the MENA region, suggesting insufficiency in TB control. Multisectoral interventions integrating strong prevention measures, standardised treatment protocols and measures to enhance treatment adherence should be implemented.
12 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A Noddings' caring theory-based intervention to enhance coping with death competence in advanced lung cancer patients: a randomized controlled trial
PURPOSE: This randomized controlled trial aimed to evaluate the effects of a Noddings' caring theory-based intervention on coping with death competence (CDC), death attitudes, and psychological distress in patients with advanced lung cancer. METHODS: Seventy inpatients were randomly allocated to the intervention group (n = 35) or the control group (n = 35). The 3-week intervention comprised modeling, dialogue, practice, and recognition phases. Outcomes, including CDC, attitude toward death, anxiety, and depression, were assessed at baseline (T0), post-intervention (T1), and 1-month follow-up (T2). RESULTS: No significant between-group differences were observed at baseline. Following the intervention, patients in the intervention group demonstrated significantly higher CDC total and subscale scores, more positive death attitudes, and lower anxiety and depression scores than those in the control group at both post-intervention and 1-month follow-up. CONCLUSION: The Noddings-based program effectively enhanced death-coping competence, improved death attitudes, and reduced psychological distress in patients with advanced lung cancer. TRIAL REGISTRATION: ChiCTR2600118530, 2/6/2026 retrospectively registered.
12 May 2026
Read appraisal →Scandinavian journal of pain
Breathing therapy as a supplement to opioid management for complex chronic non-cancer pain conditions - a systematic review
OBJECTIVES: The purpose of this systematic review was to analyze whether breathing therapy is effective in reducing pain, improving health-related quality of life, improving physical functioning/activity, and improving sleep quality in patients suffering from complex chronic non-cancer pain conditions (CNCP). CONTENT: An electronic literature search was conducted in MEDLINE, CINAHL, EMBASE, Cochrane Library, PEDro, and PsycINFO from inception to October 2024. The inclusion criteria were randomized clinical trials (RCT) examining breathing therapy as a sole or central intervention component in adult patients with CNCP. From 2,369 abstracts, a total of 10 RCTs (n=638) met the inclusion criteria. The population in the selected studies were patients with chronic neck pain, chronic low back pain, fibromyalgia, or tension-typed headache. Breathing therapy significantly improved pain and/or health-related quality of life in six studies. However, the quality of the included RCTs ranged from high to critically low, and substantial heterogeneity in participants, intervention, and methods prevented synthesis of results across studies. SUMMARY: This systematic review highlights breathing therapy as a promising pain management strategy in patients suffering from CNCP. We evaluated two RCTs examining similar daily slow diaphragmatic breathing for 15 min to have a low risk of bias; both demonstrating statistically significant relevant pain reduction up to 37 %. However, the strength of recommendations for clinical practice depends on the level of evidence as indicated by a risk of bias assessment (internal validity), consistency of results between studies, and generalizability (external validity). Based on the findings of this systematic review evidence of breathing therapy invention in patients suffering from CNCP conditions remains sparse. OUTLOOK: Striving to eliminate or minimize opioid management for complex CNCP, high-quality research is needed to reinforce the evidence base for non-pharmacological interventions such as breathing therapy to support modern pain management rather than former traditional pharmacological treatment. Trial registry number: PROSPERO #CRD42023460181.
12 May 2026
Read appraisal →JMIR medical informatics
Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation
BACKGROUND: Obstructive sleep apnea (OSA) affects nearly one billion people globally and poses a substantial public health threat. Effective and accessible methods for OSA risk identification are urgently needed. OBJECTIVE: This study aims to develop and externally validate a machine learning model derived from multi-parameter pulse oximetry (SpO2) for OSA screening, and to evaluate its performance, interpretability, and robustness across sex and age subgroups. METHODS: Of 4156 screened participants, 2195 underwent polysomnography (internal cohort) and 446 received home sleep apnea testing (external cohort). Eight SpO2-derived parameters, including oxygen desaturation index (ODI), hypoxic burden (HB), and ST90 (percentage of sleep time with SpO2 < 90%), were used to construct models. Six machine learning algorithms were trained, with F1-score as the primary metric and area under the curve as the secondary metric. Model interpretability was assessed using Shapley additive explanations and intrinsic feature importance scores. RESULTS: Nonlinear parameter-risk relationships were observed between oximetry indices and OSA probability. The 4-parameter ODI-HB-MinSpO2-ST90 model achieved optimal performance (F1-score = 0.9516, area under the curve = 0.9879), surpassing all single-parameter models. Shapley additive explanations analysis identified ODI, HB, and MinSpO2 as key predictors. The ODI-HB-MinSpO2-MeanSpO2 configuration demonstrated superior performance in female and younger subgroups, whereas the ODI-HB-MinSpO2-ST90 model remained optimal for male and older participants. Categorical boosting outperformed other algorithms across multiple metrics and remained robust in both subgroup and external validation analyses. CONCLUSIONS: The multi-parameter oximetry model based on the categorical boosting algorithm provides a simple and accurate tool for OSA screening. Sex- and age-stratified strategies can further enhance its clinical applicability.
12 May 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
Efficacy of the twin-block appliance for pediatric obstructive sleep apnea: an updated systematic review and meta-analysis
OBJECTIVE: To provide an updated and robust assessment of the efficacy of the Twin-Block appliance in the treatment of pediatric obstructive sleep apnea (OSA). A previous meta-analysis indicated a potential benefit, but was limited by the quality of available evidence and is now outdated. This study aims to synthesize all current evidence, including recent high-quality randomized controlled trials (RCTs). METHODS: This systematic review and meta-analysis was conducted following PRISMA guidelines. A comprehensive search of PubMed, Embase, Cochrane Library, and Web of Science was performed up to October 2025. We included RCTs, non-randomized controlled trials (NRCTs), and case series evaluating the effect of the Twin-Block appliance on respiratory parameters in children (< 18 years) with OSA. The primary outcome was the change in the Apnea-Hypopnea Index (AHI). A random-effects model was used to calculate the pooled Mean Difference (MD) and 95% Confidence Intervals (CIs). Sensitivity analyses were performed, and the risk of bias was assessed using design-specific tools (RoB 2, ROBINS-I, and the JBI Critical Appraisal Checklist). RESULTS: Eight studies, comprising a total of 259 patients, were included in the systematic review. The meta-analysis of seven studies (n = 219) demonstrated that Twin-Block therapy resulted in a statistically significant and clinically meaningful reduction in AHI (pooled MD: -6.68, 95% CI: -8.76 to -4.60; p < 0.0001). Significant heterogeneity was observed across studies (I² = 89.1%). A recent high-quality RCT, while not poolable due to a different outcome measure, provided mechanistic evidence by showing a significant increase in oropharyngeal airway volume with Twin-Block treatment compared to a control group. The findings remained robust in sensitivity and leave-one-out analyses. CONCLUSION: This updated meta-analysis provides strengthened evidence that the Twin-Block appliance is an effective treatment for reducing the severity of OSA in appropriately selected children. The therapeutic effect is likely mediated by structural enlargement of the upper airway. Despite the heterogeneity and limitations in the primary literature, these findings support the consideration of the Twin-Block appliance as a viable non-surgical intervention for pediatric OSA, particularly in patients with mandibular retrognathia. Future large-scale RCTs are warranted to confirm these findings and establish long-term effectiveness.
12 May 2026
Read appraisal →JMIR medical informatics
Cough Audio Recognition for Early Detection of Respiratory Diseases: Algorithm Development and Validation Study
BACKGROUND: Coughing is a common clinical symptom and a protective respiratory reflex closely associated with various respiratory system diseases. The acoustic characteristics of cough sounds are influenced by underlying pathological factors, with distinct acoustic signatures corresponding to different etiologies. Through rigorous analysis of these sounds, rapid identification and preliminary diagnosis of related conditions may be achieved. This approach holds great potential for broad application in mobile health and ubiquitous health platforms. OBJECTIVE: This study aimed to explore the application of acoustic analysis of cough sounds in the diagnosis of respiratory diseases to enhance the diagnostic efficiency of health care professionals. METHODS: In this study, we conducted extensive data collection, including voluntary cough audio recordings from patients diagnosed with respiratory diseases (eg, chronic obstructive pulmonary disease, lung cancer, COVID-19, and pneumonia) and from healthy participants. A total of 2610 audio samples were collected. We incorporated a channel attention mechanism (CAM) into the final convolutional block of each residual block in the ResNet18 neural network, thereby constructing the CAM-ResNet18 neural network model. The recorded cough audio samples were converted into spectrograms to form the input dataset for model training. The CAM-ResNet18 model was trained on the training set of this dataset, with iterative parameter adjustments until convergence was achieved. Finally, spectrograms from the test set were fed into the pretrained model for accurate classification of the cough-related conditions. RESULTS: Experimental results on the collected audio dataset demonstrate that the proposed CAM-ResNet18 model achieves an accuracy of 83.9% and an average F1-score of 82.52% in classifying 5 types of cough sounds. In comparison, the traditional ResNet18 model achieves an accuracy of 78.16% and an average F1-score of 78.29%, indicating a clear performance improvement with the integration of the CAM. CONCLUSIONS: The experimental results validate the effectiveness of the proposed method, highlighting its significant potential for application in clinical diagnosis.
12 May 2026
Read appraisal →The oncologist
Characterization and clinical management of adverse events following treatment with repotrectinib: a TRIDENT-1 analysis
BACKGROUND: Repotrectinib, a next-generation ROS1/TRK tyrosine kinase inhibitor, is approved for ROS1 fusion-positive non-small cell lung cancer and NTRK fusion-positive solid tumors. Its side effects and safety management strategies require further characterization. PATIENTS AND METHODS: The safety profile of repotrectinib (treatment-emergent/related adverse events [TEAEs/TRAEs]) was established in patients who initiated treatment at the recommended dose (160 mg daily [QD] for 14 days, then 160 mg twice daily [BID]) across all cohorts of the global, multicenter phase 1/2 TRIDENT-1 study. AE management strategies were outlined. RESULTS: In 472 patients, the most common TRAEs (dizziness [58%] and dysgeusia [50%]) were likely TRK inhibition-related. Median relative dose intensity was 90%; 14% (n = 66/472) of patients did not increase their initial QD dose to BID (mostly due to CNS AEs). Rates of dizziness (median onset, 7 days) were similar in patients with/without baseline brain metastases. Dose modifications downgraded severity or resolved dizziness in 78% of patients; 58% of patients had pharmacologic intervention without dose modification. Dizziness was downgraded/resolved in 62% (n = 120/195) of patients who did not receive dose modification or pharmacologic intervention. Treatment-related cognitive impairment and weight gain occurred in 19% and 12% of patients, respectively. Treatment-emergent withdrawal pain occurred in 14% of patients (median resolution time, 2.1 weeks). Dose interruption and reduction from TRAEs occurred in 39% and 38% of patients, respectively; 10% reported later re-escalation back to 160 mg BID. CONCLUSION: Many repotrectinib AEs, including neurological AEs secondary to TRK inhibition, were mitigated with appropriate management, including dose modification and/or pharmacologic intervention.
12 May 2026
Read appraisal →International health
Pneumonia in Ghana: prevalence, mortality and antimicrobial resistance-a systematic review and meta-analysis
Pneumonia is a major cause of morbidity and mortality in Ghana, especially among children <5 y of age and the elderly. However, comprehensive data on its prevalence and distribution remain limited. This systematic review addresses this gap by analysing the prevalence, regional variations, risk factors, antimicrobial resistance and mortality rates of pneumonia in Ghana. We searched PubMed, ScienceDirect, Web of Science and African Journals Online databases, including 21 studies with a total of 124 582 participants. A random-effects meta-analysis estimated pooled prevalence and subgroup differences based on age, region and participant type. Heterogeneity was assessed using the I2 test and meta-regression, with sensitivity analysis also conducted. Study quality was evaluated using standardized Joana Briggs Institute checklists for prevalence studies. The pooled prevalence of pneumonia in Ghana was 22.01%, with significant regional variations. The highest prevalence was observed in a study conducted in the Greater Accra and Northern regions (32.31%), while the lowest was in the Upper West (4.83%). The prevalence in children <5 y of age was 17.79%. Hospitalized patients had a higher prevalence (22.14%) than community children (21.49%). Streptococcus pneumoniae was the most common pathogen, with high resistance (>70%) to co-trimoxazole, gentamicin and tetracycline. Key risk factors included indoor air pollution and seasonal changes. The mortality rate for pneumonia was found to be 3.07%. This systematic review highlighted significant regional disparities, pathogen resistance patterns and environmental risk factors that shape the burden of pneumonia in Ghana, providing critical evidence for targeted public health strategies. The findings highlight avenues for future research, including environmental drivers of transmission, focusing on underreported regions and surveillance of antibiotic resistance of pathogens isolated from pneumonia patients.
11 May 2026
Read appraisal →Monaldi archives for chest disease = Archivio Monaldi per le malattie del torace
Prevalence of urinary incontinence in patients with chronic cough: a systematic review
Chronic cough-related diseases increase the risk of urinary incontinence (UI) due to repeated intra-abdominal pressure affecting the pelvic floor. Existing studies focus on specific diseases rather than UI as a broader consequence. To determine the prevalence of UI in patients with chronic cough-related diseases and compare it with the prevalence in healthy populations. A systematic review was conducted using Medline, Embase, Cochrane, OVID, Scopus, ProQuest, PEDro, and EBSCO. Two independent reviewers screened studies using predefined criteria in a Microsoft Excel spreadsheet. Only prevalence studies were included. Data were extracted, synthesized, and assessed using the GRADE approach for evidence quality and the STROBE checklist for reporting quality. Prevalence estimates varied widely based on demographic characteristics. UI prevalence ranged from 2.2% to 45% in pediatric patients, 30.4% to 74% in adult women, and 2.4% to 39% in adult men. The quality of evidence was low, while reporting quality was acceptable. UI is a common complication in patients with chronic cough, with a higher prevalence than in healthy populations. Due to the stigma surrounding urogenital disorders, clinicians should actively inquire about UI during patient history-taking and refer affected individuals for appropriate treatment.
11 May 2026
Read appraisal →Tobacco control
Vaping cessation interventions: a systematic review and meta-analysis
OBJECTIVE: Electronic nicotine delivery systems (ENDS), such as e-cigarettes, have surged in popularity. While long-term risks remain unclear, known dangers include nicotine addiction, E-cigarette or Vaping Use-Associated Lung Injury and potential extrapulmonary effects on the heart, immune system and neurodevelopment. Addressing ENDS addiction may require strategies akin to smoking cessation. However, evidence is lacking; there are no currently approved vaping-cessation aids. The objective of this review is to summarise the available literature addressing interventions for vaping cessation. DATA SOURCES AND STUDY SELECTION: A librarian-assisted search was conducted in MEDLINE, Embase and Scopus, as well as preprints in Europe PMC, Open Science Framework, and the grey literature searches until January 2024. Six included studies used self-report methods to determine the abstinence rate, while one study used cotinine-level-verified self-reported abstinence. We included randomised controlled trials (RCTs) that examine 'rates of vaping cessation' outcomes. An intention-to-treat approach was used for data extraction, and random-effects meta-analyses models were applied. DATA SYNTHESIS: Seven RCTs were included, which studied pharmacological therapy, digital interventions and/or educational content. The primary meta-analyses demonstrated a statistically significant increase in the odds of achieving 7-day point prevalence abstinence (PPA) (OR 1.52, 95% CI 1.15 to 2.01, number of participants=3244, moderate-certainty) and continuous abstinence (OR 2.71, 95% CI 1.31 to 5.61, number of participants=164, low-certainty) following intervention. A non-significant increase in odds was noted at 30-day PPA (OR 1.32, 95% CI 0.72 to 2.42, number of participants=1994, very low certainty). CONCLUSIONS: Vaping cessation interventions increase 7-day PPA rates at 1-12 months follow-up and this was consistent across prespecified subgroup analyses of the intervention type, outcome and duration of follow-up. More high-quality studies with reproducible findings are needed to enhance the certainty of the evidence and guide clinical interventions. PROSPERO REGISTRATION NUMBER: CRD42022383670.
11 May 2026
Read appraisal →European journal of pediatrics
Prognosis of neonates receiving invasive mechanical ventilation in low-resource settings: a systematic review and prognostic meta-analysis
Provision of invasive mechanical ventilation (IMV) in the neonatal intensive care has seen a steady rise in low-esource settings (LRS). However, outcomes among those exposed to IMV remain under-reported, with the current evidence base being restricted to single-centre observational studies, thus limiting comparative analyses and effective healthcare planning. This study aims to estimate the pooled proportion of mortality and morbidity among neonates exposed to IMV in low-resource settings. Medline, Embase, and CENTRAL were searched until 22 August 2025. Randomised and non-randomised studies were included. Two reviewers, blinded to each other, extracted data independently. Proportion-based meta-analyses using random-effects model were performed. Risk of bias was assessed using ROBINS-E, and evidence-certainty was evaluated using the GRADE approach. One hundred of 117 studies were included, with most conducted in South Asia. In-hospital mortality was reported in 68 studies (7193 neonates), with a pooled estimate of 45% (39%-50%), evidence-certainty being very low. Among the secondary outcomes, the pooled rates were as follows: bronchopulmonary dysplasia, 10% (5%-18%); intraventricular haemorrhage (any grade), 10% (5%-19%); necrotising enterocolitis (any stage), 14% (6%-31%); retinopathy of prematurity (any stage), 33% (22%-46%); ventilator-associated pneumonia, 21% (14%-29%); sepsis, 32% (25%-40%) and pulmonary haemorrhage, 9% (6%-14%). Evidence-certainty for all the secondary outcomes was also very low. Subgroup analysis comparing two distinct time epochs revealed a significant difference in mortality, 43% (36%-50%) (I2 = 93.5%) in the post-2010 epoch compared to 55% (48%-63%) (I2 = 82.9%) in the pre-2010 epoch (p = 0.004). Heterogeneous outcome definitions and predominance of unadjusted analyses across studies limit the existing evidence.Conclusions: In LRS, the mortality and morbidity rates among neonates receiving IMV remain substantially high with a modest improvement in survival in the past decade. Improving outcomes mandates moving beyond access to the provision of IMV to investing in comprehensive training and scaling up critical auxiliary resources. Future research must adopt standardised outcome definitions and adjusted analyses to precisely quantify the impact of IMV in LRS.
11 May 2026
Read appraisal →Journal of bronchology & interventional pulmonology
AIBAN-AI Bronchial Detection and Airway Navigation System
BACKGROUND: Accurate navigation within the lungs is critical for bronchoscopy procedures but remains challenging due to the complex airway anatomy. Artificial intelligence (AI) integrating real-time landmark detection and navigation guidance may enhance precision and safety. METHODS: We developed an AI Bronchial detection and Airway Navigation system (AIBAN) for bronchoscopy, combining airway lumen detection, anatomic localization, and navigation guidance. A YOLOv8-based detection module was used to identify 34 airway landmarks in real time. Next, an airway-level localization module estimated the bronchoscope's position using a hierarchical anatomic graph, identifying the deepest branch compatible with all coherent detections in each frame. A navigation module then recommended the next airway to follow along predefined anatomic paths, providing directional cues to perform a complete and systematic bronchoscopy. RESULTS: AIBAN was developed on a data set of 18 bronchoscopy videos, using 15 for training and 3 for validation. In validation, the framework correctly identified the bronchoscope's location in 91.1% of the frames. In addition, 92.6% of predictions were within one airway branch of the true location, and 94.5% were within 2 branches. The navigation guidance performance was also promising, with the system following the correct anatomic pathway in 96.2% of test videos, demonstrating reliable guidance through the lungs. CONCLUSION: AIBAN enables accurate airway detection, localization, and navigation guidance in a simulation-based setting. It can provide reliable guidance along predefined paths and precise spatial positioning and is a promising tool for medical training and has potential for future clinical implementation.
11 May 2026
Read appraisal →European heart journal
Cardiovascular benefit of continuous positive airway pressure according to high-risk obstructive sleep apnoea: a multi-trial analysis
BACKGROUND AND AIMS: Randomized trials of continuous positive airway pressure (CPAP) treatment for obstructive sleep apnoea (OSA) in patients with cardiovascular disease have not detected reduced risk of major adverse cardiovascular and cerebrovascular events (MACCEs). This study tested whether the cardiovascular benefit of CPAP occurs preferentially in high-risk OSA, characterized by greater OSA-related heart rate acceleration or hypoxaemia. METHODS: In a post hoc analysis of pooled Randomized Intervention with Continuous Positive Airway Pressure in Coronary Artery Disease and Obstructive Sleep Apnoea, Impact of Continuous Positive Airway Pressure on Patients with Acute Coronary Syndrome and Nonsleepy Obstructive Sleep Apnoea, and Sleep Apnoea Cardiovascular Endpoints Study randomized trials; outcomes were stratified by high-risk OSA status, defined by heart rate response following OSA respiratory events >9.4 b.p.m. (third tertile) or oxygen desaturation area under baseline (hypoxic burden) > 87.1% min/h (third tertile). Cox mixed models quantified the CPAP treatment effect on MACCE (including cardiovascular mortality, myocardial infarction, and stroke) within high-risk OSA and the difference vs low-risk status (primary test). Secondary analyses examined participants without excessive sleepiness (Epworth <11 points) or without increased blood pressure (systolic/diastolic <140/90 mmHg). RESULTS: In 3549 participants, 16.6% and 16.3% reached the MACCE endpoint with CPAP (n = 1778) and usual care (n = 1771), respectively. The CPAP treatment effect was greater in participants with vs without high-risk OSA [interaction hazard ratio (iHR) .69, 95% confidence interval (CI) .50-.95, Pinteraction = .024; Nhigh-risk = 1832]. The differential effect was stronger in those without excessive sleepiness (iHR .59, 95% CI .41-.84; Nhigh-risk = 1509), or without increased blood pressure (iHR .54, 95% CI .36-.81; Nhigh-risk = 1244). Continuous positive airway pressure benefits in high-risk OSA were observed alongside harm in low-risk OSA. CONCLUSIONS: Continuous positive airway pressure preferentially improves cardiovascular outcomes in high-risk OSA, while harm in low-risk OSA may counteract this effect. These findings provide a pathway to identify patients likely to benefit.
11 May 2026
Read appraisal →PloS one
Development and evaluation of an ontology for non-invasive respiratory support in acute care
Managing patients with respiratory failure increasingly involves non-invasive respiratory support (NIRS) strategies to support respiration, often preventing the need for invasive mechanical ventilation. However, despite the rapidly expanding use of NIRS, there remains a significant challenge to its optimal use across all medical circumstances. It lacks a unified ontological structure, complicating guidance on NIRS modalities across healthcare systems. In this study, we introduced NIRS ontology to support knowledge representation in acute care settings by providing a unified framework that enhances data clarity and interoperability, laying the groundwork for future clinical decision-making research. We developed NIRS ontology using the Web Ontology Language (OWL) and Protégé to organize clinical concepts and relationships. To enable rule-based clinical reasoning beyond hierarchical structures, we added Semantic Web Rule Language (SWRL) rules. We evaluated logical reasoning using a sample of 6 patient scenarios and used SPARQL queries to retrieve and test targeted inferences. The ontology has 145 classes, 11 object properties, and 18 data properties across 949 axioms that establish concept relationships. To standardize clinical concepts, we added 392 annotations, including descriptive definitions based on controlled vocabularies. SPARQL queries successfully validated all test cases (rules) by retrieving appropriate patients' outcomes: for instance, a patient treated with HFNC (high-flow nasal cannula) for 2 hours due to acute respiratory failure may avoid endotracheal intubation. This NIRS ontology formally represents domain-specific concepts, including ventilation modalities, patient characteristics, therapy parameters, and outcomes. SPARQL query evaluations across clinical scenarios confirmed the ontology's ability to support rule‑based reasoning and therapy recommendations, providing a foundation for consistent documentation practices, integration into clinical data models, and advanced analysis of NIRS outcomes. In conclusion, this proof-of-concept NIRS ontology demonstrates how clinical concepts can be formally represented and queried, offering a foundation for future validation, EHR integration, and evidence-based refinement of NIRS practices.
11 May 2026
Read appraisal →Diagnostic and interventional radiology (Ankara, Turkey)
Imaging in chronic thromboembolic pulmonary hypertension: review of the current literature
Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe, life-threatening complication of pulmonary embolism with pulmonary hypertension (PH). The combination of insufficient resolution of thrombi following pulmonary emboli and accompanying microvascular disease results in PH. Advances in imaging can offer better insight into CTEPH diagnosis and management, but lack of disease awareness among radiologists has been shown to be a cause of CTEPH misdiagnosis or delayed diagnosis. This review highlights features pertinent to CTEPH diagnosis. The primary focus is on different modalities with their distinctive signs and newly developed technologies employing artificial intelligence systems.
10 May 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
What is the most effective non-pharmacological treatment for poor sleep quality in Chronic obstructive pulmonary disease patients? a systematic review.
BACKGROUND: Chronic obstructive pulmonary disease (COPD) features poor sleep quality due to nocturnal dyspnea and psychological distress, leading to increased hospital admissions and reduced quality of life. Considering patient preference and existing evidence, non-pharmacological intervention, i.e. cognitive behavioral therapy for insomnia (CBT-I), on sleep for COPD patients had its potential efficacy. However, current guidelines do not include sleep management as standard care, and there is a lack of comprehensive reviews on this topic. This systematic review hence evaluated the effectiveness of non-pharmacological treatments for poor sleep in COPD patients. METHODS: Literature identification was conducted through MEDLINE, CINHAL PLUS, AMED and ScienceDirect under PRISMA guidance. Randomized controlled trials, cohort studies and cross-sectional studies regarding poor sleep quality among COPD population were retrieved. Abstract, title and full text screening were performed by the author and assessed by a second reviewer. Risk of bias and quality appraisal was done with JBI scale. RESULTS: 12 articles (783 patients) were identified, including 9 randomized controlled trials, 1 cohort study and 2 cross-sectional studies. The risk of bias (quality) was ranked: low (high) for 3, moderate (moderate) for 7 and high (low) for 2. Several interventions were identified (number of study): CBT-I (2) and cognitive behavioral therapy (1), pulmonary rehabilitation (PR) (2), progressive muscle relaxation technique (PMRT) (2), non-invasive ventilation (NIV) (3), relaxation exercise (RE) (1) and therapeutic touch (TT) (1). Significant improvement in sleep quality was reported in PMRT, TT and RE. CONCLUSION: PMRT, TT and RE showed improvement in sleep quality in COPD patients yet unable to rank their effectiveness as heterogeneous outcome measures across studies. No substantial evidence was capable to demonstrate effectiveness of CBT-I, CBT, PR and NIV to explicit COPD patients. Moreover, future research including combination of non-pharmacological treatments is needed.
10 May 2026
Read appraisal →The Journal of asthma : official journal of the Association for the Care of Asthma
Prediction model for cardiovascular mortality risk in asthma patients based on machine learning
BACKGROUND: Asthma is a common chronic respiratory disease, cardiovascular disease (CVD) mortality constitutes a major public health concern. At present, no personalized tools are available to accurately estimate CVD mortality risk in this population. Accordingly, a machine learning model was developed to predict incident CVD mortality of asthma. METHODS: Data were extracted from NHANES (2007-2012). Predictors were selected by LASSO and multivariable Cox regression. Discrimination and calibration were quantified with ROC curves and calibration curves, nomogram and web calculator were subsequently deployed. Incremental predictive value relative to ASCVD risk and Framingham risk scores were assessed with the integrated discrimination improvement and net reclassification improvement. Six machine learning algorithms were trained, and feature importance of the optimal model was interpreted with SHAP values. RESULTS: Among 2,033 adults participants with asthma, with mean age 44 years, and they comprised 834 males (41.02%) and 1199 females (58.98%). Twelve variables (age, sex, smoking, hypertension, diabetes, CKD, SBP, LYM, UA, K, FENO, and PEF) were retained as predictors of CVD mortality. Calibration was stable across training sets (C-index = 0.863) and validation sets (C-index = 0.832). The pulmonary function test augmented NHANES model outperformed both ASCVD risk and Framingham risk scores. RF and XGB model achieved the highest discrimination. CONCLUSION: A machine learning model integrating routine clinical and pulmonary function test indices was developed. The model enables accurate estimation of long term CVD mortality risk with asthma, facilitating early identification of high risk individuals and informing personalized preventive strategies.
9 May 2026
Read appraisal →Annals of surgical oncology
CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information
BACKGROUND: Accurate preoperative prediction of visceral pleural invasion (VPI) in lung adenocarcinoma is essential for guiding surgical decision-making. However, existing prediction models often sacrifice specificity when optimized for high sensitivity, increasing the risk of overtreatment. This study aimed to develop a computed tomography (CT)-based deep learning (DL) model that improves specificity by excluding chest wall information. METHODS: We retrospectively enrolled 835 patients with pathologically confirmed lung adenocarcinoma who underwent complete resection at two medical centers. A VPI-DL model was developed using a four-layer convolutional neural network incorporating a novel attention mechanism trained on chest-wall-masked CT inputs. The model was trained on 692 cases and externally validated on an independent cohort of 143 patients. Performance was benchmarked against the consolidation-to-tumor ratio (CTR), a published DL model, and assessments by three thoracic surgeons. Evaluation metrics included area under the curve (AUC), sensitivity, specificity, and accuracy under a high-sensitivity operating threshold. RESULTS: In external validation, VPI-DL achieved an AUC of 0.91, sensitivity of 91%, specificity of 82%, and accuracy of 83%, outperforming the CTR (specificity 63%), the previously published model (specificity 54%), and thoracic surgeons (specificity 57%) at comparable sensitivity levels. The attention-guided architecture effectively reduced spurious correlations and improved interpretability. CONCLUSION: The proposed VPI-DL model improves the specificity of VPI prediction while preserving high sensitivity. By intentionally excluding chest wall information, the model offers a robust and interpretable tool to aid preoperative planning and minimize the risk of both under- and overtreatment in early-stage lung adenocarcinoma.
6 May 2026
Read appraisal →Cell
Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes
Identifying drugs that reverse disease-associated transcriptomic features has been widely explored for drug repurposing, but its potential for de novo drug discovery remains underexplored. Here, we present gene expression profile predictor on chemical structures (GPS), a deep-learning-based drug discovery platform, guided by transcriptomic features, that screens large compound libraries and optimizes lead molecules. We first develop a model that captures transcriptomic perturbation signatures solely from chemical structures and deploy it to library compounds. We refine scoring methods and employ a tree-search method for optimization. By incorporating structure-gene-activity relationships, we uncover drug mechanisms from transcriptomic data. We evaluate GPS across multiple diseases and conduct extensive validation in two cases. In hepatocellular carcinoma, we discover two unique compound series with favorable cellular selectivity and in vivo efficacy. In idiopathic pulmonary fibrosis, we identify one repurposing candidate and one novel anti-fibrotic compound by reversing gene expression of multiple distinct cell types derived from single-cell transcriptomics.
5 May 2026
Read appraisal →Journal of medical systems
Automated data extraction from electronic medical records for pragmatic clinical trials
Data collection in randomized trials is expensive and labor intensive. With the rise in ongoing pragmatic trials, the use of electronic medical records (EMR) as a source of data has increased. Although potentially faster and cheaper, EMR use can lead to errors. Therefore, to ensure accurate data collection and to avoid systematic errors we performed a study comparing automated data extraction (ADE) with manual data extraction (MDE). We performed a retrospective cohort study to compare the accuracy of ADE using Structured Query Language with MDE by blinded physicians from our EMR. We tested the interrater agreement and intraclass correlation coefficient of clinical baseline data and outcomes of a random sample of 30 patients admitted to the ICU, on mechanical ventilation, requiring opioids for analgosedation for an upcoming pragmatic clinical trial. Key data compared included, but not limited to, patient's demographics, laboratory and vital signs, daily morphine milligram equivalent (MME), days alive and free of mechanical ventilation, days alive and free of hospitalization, days alive and free of ICU, days alive and free of vasopressors, and death. Among 238 patients screened over 1-month period, 72 fulfilled inclusion criteria and 30 were randomly selected to be included in the evaluation. We blindly collected 1320 baseline data, 2160 categorical outcomes and 705 continuous outcomes for a total of 4185 data points. The intraclass correlation coefficient and the Cohen's Kappa were perfect or almost perfect for all data, including outcomes such as daily MME, days alive and free of mechanical ventilation, days alive and free of ICU and days alive and free of hospital with p < 0.001. Among all rechecked data, the ADE was correct in 53 (77.9%) of cases, while MDE in 15 (22.1%). The inaccurate data collected by ADE accounted for 0.36% of the total data-points. The performance of ADE had almost perfect agreement for all outcomes and when rechecking for disagreements, it was more accurate than MDE.
5 May 2026
Read appraisal →Journal of proteome research
Stability-Based Machine Learning Identifies a Minimal Two-Protein Serum Signature for Early Silicosis
BACKGROUND: The early diagnosis of silicosis, an irreversible fibrotic lung disease, is challenged by the low sensitivity of current radiological methods in early-stage disease and their susceptibility to interobserver variability. Consequently, a pressing need exists for noninvasive, objective biomarkers to facilitate timely detection and intervention. METHODS: We employed a multistage study design comprising a discovery cohort (57 Stage I silicosis patients, 57 matched controls) and an independent, unmatched validation cohort (40 patients, 40 controls). Serum protein profiles were generated using Olink targeted proteomics. We utilized a rigorous, stability-based machine learning framework, which integrated Lasso, Random Forest, and SVM-RFE algorithms over 100 iterations, to perform feature selection and identify a robust biomarker signature from the discovery cohort. Based on the selected features, a logistic regression model was subsequently constructed, and its performance was evaluated using both internal and external validation. RESULTS: Our discovery strategy identified a two-protein signature comprising IL8 and CCL3. This signature demonstrated excellent diagnostic performance in the discovery cohort, achieving a cross-validation AUC of 0.986 (95% CI: 0.975-1.000). Importantly, the model's robustness was confirmed in the heterogeneous validation cohort, where it achieved an outstanding AUC of 0.973 (95% CI: 0.936-1.000), with 95.0% specificity and 77.5% sensitivity. Bioinformatic analysis revealed that decreased serum levels of IL8 and CCL3 were associated with silicosis, providing novel diagnostic biomarkers and highlighting a complex, paradoxical shift in circulating chemokines during early-stage disease.
4 May 2026
Read appraisal →Annals of surgical oncology
Computed Tomography-Pathology Deep Learning Model for the Precise Prediction of Recurrence in Pathological Stage IA Lung Adenocarcinoma
BACKGROUND: The postoperative prognosis of pathological stage IA lung adenocarcinoma (LUAD) exhibits significant heterogeneity. While the tumor node metastasis (TNM) staging system offers limited recurrence prediction capability, this study aims to develop a precise deep learning model for prognostic stratification. PATIENTS AND METHODS: We retrospectively analyzed a consecutive cohort of patients with pathological stage IA LUAD who underwent surgical resection at Zhongshan Hospital Fudan University. Our novel ResNet 3D-Pathology Fusion (Res3D-PF) model-integrating a three-dimensional ResNet backbone with an image-pathology fusion module-was developed to predict recurrence-free survival (RFS) using preoperative computed tomography (CT) images and International Association for the Study of Lung Cancer (IASLC) grading. Model performance was evaluated through receiver operating characteristic curve analysis, with independent RFS predictors identified via multivariable Cox regression. RESULTS: Among 551 patients with stage IA LUAD (median age 61 years; 339 women) divided into training (n = 368) and validation (n = 183) sets, the CT-pathology fusion model achieved superior predictive performance. In the validation cohort, Res3D-PF significantly outperformed the 8th T-stage (AUC 0.837 vs. 0.660, p = 0.001) and IASLC grade (AUC 0.837 vs. 0.684, p = 0.015) for 5-year RFS prediction. Multivariable analysis confirmed Res3D-PF as an independent prognostic factor (HR 15.772, 95% CI 3.384-73.508; p < 0.001). Model-stratified high-risk patients demonstrated significantly reduced 5-year RFS (73.1% vs. low-risk 98.5%, p < 0.001) and shorter median RFS (74.4 vs. 96.2 months, p < 0.001). CONCLUSIONS: We developed and validated a CT-pathology deep learning model that outperforms conventional TNM staging and IASLC grading for predicting postoperative recurrence in stage IA LUAD. This approach enables individualized risk stratification to guide precision treatment strategies.
4 May 2026
Read appraisal →Journal of occupational and environmental medicine
Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework
OBJECTIVE: Pneumoconiosis is a common and highly hazardous occupational disease. The staging of pneumoconiosis is mainly carried out by experienced doctors on the basis of the shadows and textures on lung x-ray films. Despite well-defined criteria, the process remains influenced by individual clinical judgment. METHODS: To improve the subjective process of pneumoconiosis diagnosis, this study proposes a new deep learning framework for pneumoconiosis staging framework using MEA-Net and LAP-Net. RESULTS: The experimental results show that the accuracy, precision, recall, specificity, F1-score, and area under the curve in the four-stage classification reached 95.24%, 95.15%, 95.15%, 90.58%, 94.85%, and 98.87%, respectively. CONCLUSION: The proposed method can help doctors to identify the different stages of pneumoconiosis more accurately in the diagnosis of the disease.
4 May 2026
Read appraisal →IEEE transactions on bio-medical engineering
Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection
OBJECTIVE: Accurate detection of adventitious respiratory sounds, such as wheezes and crackles, is essential for diagnosing and managing respiratory conditions. This study introduces a multi-stage, confidence-driven framework for automated pediatric auscultation analysis, performing a three-way classification of normal, wheeze, and crackle sounds to improve diagnostic accuracy. METHODS: We develop a comprehensive pipeline integrating anomaly-specific segment selection, segment-level classification, and confidence-based fusion. Our contrastive variational recurrent neural network (CVRNN) enhances feature extraction, while a confidence-weighted aggregation strategy refines final predictions. The system is validated using a diverse pediatric dataset from 742 subjects (aged 1-59 months) from seven countries. RESULTS: The multi-level framework is evaluated across three stages. The anomaly-specific segment selection achieves 98.47 $\%$ recall, identifying adventitious regions. Next, segment-level classifiers improve sensitivity, achieving balanced accuracies of 72.15 $\%$ (wheeze) and 68.1 $\%$ (crackle). This performance surpasses state of the art systems on the same dataset and demonstrates enhanced balanced performance in detecting both crackle and wheeze sounds, which present different challenges to automated systems given their markedly different acoustic profiles. Finally, the confidence-driven fusion outperforms traditional aggregation methods, yielding a final three-way classification of 62.12 $\%$. CONCLUSION: Our confidence-based multi-stage approach enhances automated respiratory sound classification by prioritizing high-certainty segment predictions, aligning with expert physician annotations. SIGNIFICANCE: This framework advances computer-aided respiratory diagnostics, improving early detection and monitoring of pediatric respiratory conditions. By integrating expert-inspired segmentation with machine learning-driven confidence estimation, it has potential to enhance clinical workflows and screening for pulmonary diseases, particularly in resource-limited settings.
4 May 2026
Read appraisal →Scientific reports
Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds
A prompt and precise diagnosis is necessary to lessen the worldwide burden of asthma, a common chronic respiratory condition. Spirometry is one of the resource-intensive conventional methods that frequently misclassifies cases because of overlapping symptoms with other respiratory illnesses. The Asthma Detection Dataset v2, which is openly accessible and comprises actual audio recordings of respiratory noises, is used in this work to demonstrate a hybrid deep learning architecture for automated asthma detection. After applying thorough preprocessing, which includes denoising, segmentation, and augmentation, the suggested pipeline employs a dual-path feature extraction technique that combines pretrained YAMNet embeddings with handmade acoustic descriptors (MFCCs, chromagram, ZCR, and spectral features). Atrous Spatial Pyramid Pooling (ASPP) and Squeeze-and-Excitation (SE) modules are used to further improve these embeddings, and a Multi-Layer Perceptron is used for classification. The proposed model achieved high performance, with an accuracy of 98.6% ± 0.14, an F1-score of 97.5% ± 0.14, and a macro-AUC of 99.1% ± 0.14 using stratified 5-fold cross-validation. SHAP-based interpretability and visualization (waveforms, spectrograms, t-SNE) verified that the model records clinically significant auditory biomarkers such as crackles and wheezes. These results indicate the potential of the proposed model as an explainable and scalable computer-aided asthma diagnostic tool.
3 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Oscillometry for the diagnosis of asthma in children: a systematic review
BACKGROUND: Diagnosing asthma in children and young people (CYP) remains challenging. Oscillometry is a promising tool and is feasible from 2 years of age. European Respiratory Society (ERS) technical standards and bronchodilator response (BDR) oscillometry thresholds have been published, but diagnostic accuracy is not established. METHODS: We systematically reviewed studies comparing oscillometry and spirometry in CYP under investigation for asthma. Reference standards were positive BDR or positive methacholine challenge test (MCT). Primary aims were to investigate the sensitivity and specificity of current ERS oscillometry thresholds (>40% decrease in resistance at 5 Hz (R 5), >50% increase in reactance at 5 Hz (X 5) or >80% decrease in the area under the reactance curve); secondary aims were to identify oscillometry threshold values optimising both sensitivity and specificity. RESULTS: 11 studies were included; six (n=992 CYP) utilised BDR and five (n=531 CYP) MCT as reference standard. Meta-analysis was not possible due to heterogeneity of results reported. In two studies using current ERS BDR thresholds, zero sensitivity and high specificity (>85%) were observed. In weighted regression analyses of BDR studies, a 17.0% decrease in resistance at 5-6 Hz had sensitivity and specificity of 71.6% (95% CI 69.7-73.7%); a 20.2% increase in X 5 had sensitivity and specificity of 68.6% (95% CI 66.6-70.8%). Similarly, 27.7% increase in R 5 had sensitivity and specificity of 73.6% (95% CI 71.9-75.3%) for MCT. CONCLUSION: Currently recommended ERS thresholds for oscillometry BDR have low sensitivity. Proposed thresholds for defining positive BDR and MCT by oscillometry require prospective validation and adoption of standards for measuring and reporting oscillometry parameters in future diagnostic comparative studies.
3 May 2026
Read appraisal →Current allergy and asthma reports
Interception of Regulatory RNAs on TGF-β Signaling in the Pathogenesis of Idiopathic Pulmonary Fibrosis: A Systematic Review
PURPOSE OF REVIEW: Idiopathic Pulmonary Fibrosis is a type of interstitial lung disease characterized by lung scarring due to excessive extracellular matrix (ECM) deposition. The transforming growth factor-beta (TGF-β) signaling pathway is the master regulator of fibrosis, driving myofibroblast activation and differentiation, epithelial-mesenchymal transition (EMT), and inhibition of ECM degradation. While individual classes of non-coding RNAs (ncRNAs) have been studied in IPF, a comprehensive understanding of how microRNAs (miRNAs), circular RNAs (circRNAs), and long non-coding RNAs (LncRNAs) collectively regulate canonical TGF-β signaling remains lacking. Addressing this gap, this systematic review was conducted which presents current evidence on the integrated roles of ncRNAs in modulating TGF-β signaling in IPF pathogenesis. RECENT FINDINGS: A comprehensive search of PubMed and Web of Science databases (2015-2025), identified 45 eligible studies. miRNAs (19 antifibrotic, 11 profibrotic) directly targeted the core components of the TGF-β signaling pathway, whereas circRNAs (5 antifibrotic, 5 profibrotic) and LncRNAs (6 profibrotic) acted as competing endogenous RNAs and sponged their target miRNAs. Notably, all identified LncRNAs were profibrotic, amplifying the fibrotic signaling pathway. We found that ncRNAs critically fine-tune TGF-β signaling at multiple regulatory nodes, including ligand activation, receptor expression, Smad protein phosphorylation, nuclear translocation, and inhibitory feedback mechanism. The ncRNA-mediated regulatory mechanisms provide valuable insight into IPF pathogenesis and may help identify potential biomarkers and therapeutic targets. Future efforts should prioritize validating in vitro and animal model data in human samples, integrating regulatory network analysis, in vivo functional validation of RNA-based therapeutics, and exploring therapeutic delivery systems to develop an effective ncRNA-based antifibrotic therapy.
3 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Rural versus urban living and COPD: a systematic review
Chronic obstructive pulmonary disease (COPD) is a leading cause of global morbidity and mortality. Emerging evidence suggests disparities in COPD outcomes between rural and urban populations, but no prior review has synthesised these differences globally. Five databases (Medline, Embase, Emcare, CINAHL and Cochrane Central) were searched in May 2025. Eligible peer-reviewed studies directly compared rural and urban populations in at least one of four measures of COPD burden: prevalence, symptom burden, exacerbations or mortality. Study quality was assessed and narrative synthesis was conducted due to heterogeneity in outcome measures. Of 1339 screened studies, 32 met inclusion criteria, spanning 13 countries. COPD prevalence was higher rurally in 83% (15/18) of studies, with 11/15 demonstrating statistical significance. This pattern was consistent across geographical distributions. Total exacerbation rates were higher rurally in 60% (3/5) of studies, although hospitalisations varied significantly. 50% (6/12) of studies reported higher hospitalisation rates in urban areas and 5/12 studies reporting higher rates in rural areas. 86% (6/7) of studies demonstrated higher mortality rurally and symptom burden was higher amongst rural residents in 67% (4/6) of studies; however, the majority of these were conducted in the USA. This review highlights consistent rural-urban inequalities in COPD prevalence and outcomes, reflecting the impact of healthcare inequities, socioeconomic deprivation and environmental exposures on COPD burden in rural areas. Targeted interventions promoting equitable healthcare access, health education, transition to cleaner fuels and rural access to smoking cessation and pulmonary rehabilitation services are essential to mitigate these disparities and improve outcomes in rural populations.
3 May 2026
Read appraisal →PloS one
Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification
One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the imaging modalities is always subjective, and this paves the way for deep learning assisted computer aided techniques. Still, the accuracy of such a technique is the major concern. This research work attempts to enhance lung cancer diagnostics from histopathological images using a previously unaddressed combination of multiple self-supervised learning techniques, filter-based feature selection, and Vision Graph Convolutional Networks. The main contribution lies in optimized feature fusion, and it brings complementary strengths of three different self-supervised learning approaches- contrastive alignment, redundancy reduction, and semantic grouping. The first step involves extracting the key features from histopathological images using a custom Convolutional Neural Network. Three complementary self-supervised learning methods - Deep Cluster, Bootstrap Your Own Latent, and Simple Framework for Contrastive Learning of Visual Representations - are then used to refine each of these features separately. Following optimization, the improved features are combined, and the most significant features from the combined set are found and preserved using a filter-based feature selection technique called Minimum Redundancy Maximum Relevance. Vision Graph Convolutional Neural Network is used as a classifier. Initial experiments are carried out on the 691 histopathological images extracted from the publicly available LungHist700 dataset. This dataset contains histopathological images under three categories: 151 normal subjects, 280 lung adenocarcinoma subjects, and 260 lung squamous cell carcinoma subjects. The proposed approach provided a balanced accuracy of 97% while the plain Vision Graph Convolution Neural Network offers only an 86% balanced accuracy score. Further validation is performed using two more publicly available datasets, namely LC25000 and TCGA UT datasets. The experimental results demonstrate the enhanced lung cancer prediction performance of the proposed approach in all three datasets.
2 May 2026
Read appraisal →The Cochrane database of systematic reviews
Vitamin D for preventing acute respiratory infections in children up to five years of age
RATIONALE: Acute respiratory infections (ARIs) are a leading cause of morbidity and mortality in children under five years of age and contribute to healthcare visits and hospitalisations globally. Vitamin D deficiency is common amongst pregnant women and young children. Given that vitamin D supplementation is safe, affordable, and easy to administer, its potential to reduce ARI-related healthcare visits holds substantial public health relevance. This review evaluates the benefit and harm of vitamin D supplementation in preventing ARI-related healthcare visits in young children, aiming to inform supplementation policies during pregnancy and early childhood. OBJECTIVES: To determine the benefit and harm of vitamin D supplementation for preventing ARIs in children up to five years of age. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, CINAHL, Web of Science, WHO Global Index Medicus, and four clinical trial registries on 18 March 2025. ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) comparing vitamin D to placebo, or a higher dose (≥ 1000 IU) versus a lower dose (≤ 1000 IU) administered from early pregnancy through to five years of age. We excluded studies involving children with chronic respiratory or systemic conditions. OUTCOMES: Critical outcomes: 1) healthcare visits made by children for an ARI: (a) the proportion of children with healthcare visits made for ARIs; (b) the mean number of healthcare visits made for ARIs per child; and 2) hypercalcaemia: the number of pregnant women and children with hypercalcaemia. RISK OF BIAS: We assessed risk of bias using the Cochrane risk of bias (RoB) 1 tool for outcomes reported in the summary of findings tables. SYNTHESIS METHODS: We synthesised outcome data using meta-analyses, calculating risk ratios (RR) for dichotomous, and mean differences (MD) for continuous outcomes, each with 95% confidence intervals (CI). Where meta-analysis was not feasible, we summarised the results narratively. We used GRADE to assess the certainty of the evidence for pre-specified outcomes. INCLUDED STUDIES: We included 107 RCTs involving 31,521 participants. Vitamin D supplementation was administered during pregnancy, early childhood, or both, and in some cases to both infants and lactating mothers. Interventions used vitamin D2, D3, both, or unspecified forms, with dosages ranging from daily administration to large single or quarterly boluses. Most were delivered orally, typically on a daily schedule, but also weekly or quarterly, over durations ranging from a single dose to 18 months. Settings included hospitals, day-care centres, communities, and homes. SYNTHESIS OF RESULTS: Vitamin D supplementation may result in a slight reduction in the proportion of children who make ARI-related healthcare visits compared to placebo (risk ratio (RR) 0.95, 95% confidence interval (CI) 0.91 to 1.00; P = 0.03; 10 studies, 2447 participants; low-certainty evidence), but probably does not reduce the mean number of ARI-related healthcare visits per child (mean difference (MD) 0.07, 95% CI -0.06 to 0.20; P = 0.32; 3 studies, 561 participants; moderate-certainty evidence). Comparisons between higher and lower doses of vitamin D probably do not reduce the proportion of children making ARI-related healthcare visits (RR 0.94, 95% CI 0.81 to 1.10; P = 0.46; 2 studies, 1382 participants; moderate-certainty evidence), and do not reduce the mean number of ARI-related healthcare visits per child (MD -0.10, 95% CI -0.59 to 0.39; P = 0.69; 1 study, 579 participants; low-certainty evidence). No cases of hypercalcaemia were reported among pregnant women in trials comparing vitamin D supplementation with placebo. In higher versus lower dose comparisons, supplementation may result in little to no difference in the risk of hypercalcaemia among pregnant women (RR 1.61, 95% CI 0.60 to 4.31; P = 0.34; 6 studies, 2379 participants; low-certainty evidence). Similarly, the evidence that vitamin D supplementation has little to no effect on the risk of hypercalcaemia in children, when compared with placebo, is very uncertain (RR 0.81, 95% CI 0.53 to 1.24; 7 studies, 1542 participants; very low-certainty evidence) and may result in little to no difference in the risk of hypercalcaemia in children when higher doses are compared to lower doses (RR 1.23, 95% CI 0.82 to 1.85; 7 studies, 1287 participants; low-certainty evidence). AUTHORS' CONCLUSIONS: We found low-certainty evidence that vitamin D supplementation during pregnancy or early childhood may result in a slight reduction in the proportion of children under five years of age who make ARI-related healthcare visits, but probably does not reduce the mean number of healthcare visits for ARIs per child. This finding highlights the need for large, well-designed, placebo-controlled trials to confirm the potential benefit. We found moderate-certainty evidence that higher-dose vitamin D supplementation, compared with lower doses, probably does not reduce the proportion of children making healthcare visits for ARIs and does not reduce the mean number of ARI-related healthcare visits per child. Hypercalcaemia occurs infrequently in both pregnant women and children receiving vitamin D supplementation. Where hypercalcaemia could be measured, specifically in children receiving vitamin D versus placebo, vitamin D supplementation may have little to no effect on the risk of hypercalcaemia (very low-certainty evidence). In both children and pregnant women, a higher dose of vitamin D compared to a lower dose may result in little to no difference in the risk of hypercalcaemia (low-certainty evidence). FUNDING: This Cochrane review had no dedicated funding. REGISTRATION: Protocol (2022) DOI: 10.1002/14651858.CD015111.
2 May 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Evaluation of an adaptive system for breathing control in closed-loop diaphragm pacing
OBJECTIVES: Existing diaphragm pacing (DP) system use an open-loop control method with a fixed stimulation mode to control breathing. It requires doctors to manually adjust stimulation parameters to meet the patient's ventilation needs. METHODS: A neural network adaptive controller was tested to control breathing in DP system. For the diaphragm motion model, the respiratory airflow of healthy adults and rabbits was collected and compared with the simulation calculation results to verify the accuracy of the model. The performance of the adaptive controller was evaluated comparatively with that of the PID controller. Adaptive controller consists of a neural network and adaptively adjusts stimulation parameters to produce the desired respiratory volume waveform. Superiority of the output performance of the adaptive controller was further studied by setting various diaphragm model parameters. We further verified the feasibility of the adaptive controller through animal testing. RESULTS: The adaptive controller is better than the PID controller in maintaining the stability of the desired breath volume. Application of the adaptive controller can reduce the root mean square (RMS) error between the desired breath volume and the actual value to less than 6 %. CONCLUSIONS: This study demonstrates the potential application of adaptive controllers in closed-loop DP systems.
27 Apr 2026
Read appraisal →Journal of ethnopharmacology
Salvia plebeia R.Br.: A comprehensive review of its ethnopharmacology, phytochemistry, pharmacological activities, quality control, and current applications.
ETHNOPHARMACOLOGICAL RELEVANCE: Salvia plebeia R.Br. (Lamiaceae), the dried aerial parts, has been traditionally used across China and East/South Asia to treat sore throat, bronchitis, nephritis edema, abscesses, mastitis, hemorrhoids, and bleeding disorders, consistent with traditional indications of heat-clearing, detoxifying, cooling blood, and promoting diuresis. AIM OF THE REVIEW: This study provides a systematic overview of the ethnopharmacology, phytochemistry, pharmacological activities, quality control, and current applications of S. plebeia, while highlighting current research gaps and future perspectives. MATERIALS AND METHODS: A comprehensive literature search was conducted using multiple scientific databases, including Web of Science, PubMed, Elsevier, ACS, CNKI, and Google Scholar, covering publications from database inception to October 2025. Relevant information on the ethnopharmacology, phytochemistry, pharmacological activities, quality control, and applications of S. plebeia was systematically collected, screened, and synthesized according to predefined inclusion/exclusion criteria. RESULTS: To date, 325 constituents have been identified, dominated by flavonoids, phenolic acids, terpenoids, volatile oils, sterols, lignans, and others. Extracts and purified compounds show tradition-relevant anti-inflammatory, antiviral, immunoregulatory, and antioxidant activities, while other reported effects (hepatoprotective, hypolipidemic, cardiovascular-protective, sedative/anticonvulsant, anticancer, and anti-photoaging) are presented as exploratory findings. Quality evaluation evolved from UV and HPLC to UPLC-MS fingerprinting using flavonoids and sesquiterpenes as markers. In practical applications, S. plebeia has been used in medicinal preparations for the management of pharyngitis, bronchitis, nephritis, dermatitis, and anorectal inflammation. In food sector, it has been developed into functional products, including health teas, chewable or effervescent tablets, composite beverages, and co-fermented vinegars. CONCLUSION: Although S. plebeia shows broad pharmacological potential, current investigations remain constrained by incomplete chemical space coverage, limited pharmacokinetic and toxicological characterization, and insufficient well-designed clinical evidence. Future research should focus on comprehensive constituent profiling, mechanism-oriented validation aligned with traditional indications, standardized quality markers/fingerprints, and rigorous PK-toxicology-clinical translation, to promote safe, effective, and evidence-based utilization of S. plebeia in medicine and functional food development.
27 Apr 2026
Read appraisal →ACS sensors
Wearable Multimodal Detection System for Real-Time In Situ Analysis of Exhaled Breath Condensate
Exhaled breath condensate (EBC) analysis, a promising noninvasive respiratory monitoring method, has emerged as a pivotal technique for assessing the health status of patients with respiratory disorders and is widely used in clinical research and daily health management. However, conventional analytical methods face challenges in real-time in situ detection of physicochemical indicators and active EBC collection in a power-free way. Herein, a wearable multimodal detection system (WMDS) with efficient collection and real-time analysis of EBC is developed. Specifically, the WMDS consists of a bio-inspired collector, an electrochemical sensor (EBC analysis), a respiratory sensor (humidity and respiratory rate), a temperature sensor, and a flexible printed circuit board. The miniature-sized collector with a cactus spine-like structure can actively harvest 4.1 μL of EBC within 1 min without power consumption. Leveraging self-developed sensors and wireless data transmission circuitry, the WMDS enables real-time in situ monitoring of multimodal EBC analytes (hydrogen peroxide, nitrite, urea) and respiratory parameters (temperature, humidity, and rate). Remarkably, the WMDS exhibits dual-range detection capability covering both physiological and pathological conditions: the low-concentration range of 0-500 μmol/L is applicable for routine health monitoring and early disease screening, with detection limits (LODs) of 0.209, 0.155, and 0.573 μmol/L and sensitivities of 2.7 × 10-2, 3.4 × 10-2, and 9.0 × 10-3 μA/(μmol/L) for EBC analytes; the high-concentration range exceeding 500 μmol/L is designed for severe pathological condition detection, where LODs and sensitivities are 302.2, 278.7, 325.2 μmol/L and 1.9 × 10-2, 1.3 × 10-2, 3.9 × 10-3nA/(μmol/L) respectively. As a proof-of-concept, the WMDS is applied to on-body respiratory monitoring, validating its potential application in real-time in situ health monitoring.
26 Apr 2026
Read appraisal →ACS sensors
Laser-Engineered MXene Heterostructure for Wearable Ammonia Sensors
Exhaled ammonia is a vital biomarker for clinical diagnosis of hepatorenal dysfunction and metabolic disorders. However, conventional sensors are often limited by complex fabrication processes, high operating temperatures, and particularly, compromised selectivity under high-humidity conditions. To overcome these challenges, we developed a laser-assisted heterostructure engineering strategy that enables in situ construction of Schottky barrier in MXene/polyacrylonitrile (PANF) nanofiber membranes, termed as a laser-engineered MXene (LEM) sensor. The laser micropatterning technique provides three key innovations: (1) kinetically controlled deposition of MXene (Ti3C2Tx) with minimal oxidation, (2) self-aligned formation of Schottky barriers through interfacial TiO2/MXene heterojunctions, and (3) creation of hierarchical gas transport channels within the 3D nanofibrous matrix. The optimized LEM sensor exhibits outstanding sensitivity (2.5% ppm-1), a low detection limit (0.2 ppm) at room temperature (25 °C), and humidity operation stability (> 10% response at 90% relative humidity). This performance surpasses that of conventional MXene-based sensors by fivefold in sensitivity without the need for thermal activation. Mechanism studies reveal that the laser-induced heterointerface facilitates charge transfer-dominated ammonia adsorption and desorption kinetics, while the nanofibrous architecture ensures molecular accessibility to active sites. This ambient-processable and scalable fabrication method paves the way for advanced wearable breath analyzers by combining manufacturing versatility, humidity-resistant excellent selectivity, and energy-efficient operation.
26 Apr 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
ACT on Vaping: Pilot Randomized Controlled Trial of a Novel Digital Health App With Text Messaging for Young Adult Vaping Cessation
BACKGROUND: There is no published evidence to support the efficacy of any digital vaping cessation program for young adults (YAs) at differing levels of readiness to quit. In this pilot randomized controlled trial, we evaluated the preliminary acceptability and efficacy of a program for vaping cessation based on acceptance and commitment therapy (ACT on Vaping), delivered via a smartphone app and text messaging. METHODS: YAs aged 18-30 (n = 61) were randomized 1:1 to ACT on Vaping (n = 31) or incentivized text message control (n = 30). Outcome data were collected at 3 months post-randomization. Results were compared against a priori benchmarks for acceptability (satisfaction of ≥3.5 on 5-point scale) and efficacy relative to control (meeting at least one of three): ≥1-point difference in Contemplation Ladder change scores; ≥5 percentage difference in 24-hour quit attempts, ≥5 percentage difference in cotinine-confirmed 30-day point prevalence abstinence (PPA) from all non-therapeutic nicotine/tobacco. RESULTS: Satisfaction with ACT on Vaping averaged 3.8, exceeding the acceptability benchmark. A higher proportion of participants in the ACT on Vaping arm reported a 24-hour quit attempt (87.5% vs. 75.9%), exceeding the efficacy benchmark. Both changes in quit readiness (+0.96 in ACT on Vaping vs. +0.72 in control) and cotinine-confirmed 30-day PPA (4.2% in ACT on Vaping vs. 0% in control) were descriptively higher for ACT on Vaping but did not reach the benchmark level for efficacy. CONCLUSIONS: ACT on Vaping had promising acceptability and preliminary efficacy. A fully powered trial of ACT on Vaping is warranted to evaluate its efficacy. IMPLICATIONS: Digital interventions are a promising yet under-researched approach for reaching and supporting YAs to quit vaping. This proof-of-concept pilot randomized controlled trial evaluated a novel mobile health application and associated text messaging program (ACT on Vaping) for young adult vaping cessation and found preliminary evidence for acceptability and efficacy relative to an incentivized text message control arm, warranting evaluation in a fully powered trial as a next step. TRIAL REGISTRATION: NCT05897242.
26 Apr 2026
Read appraisal →PloS one
Biomarkers of mitochondrial dynamics in idiopathic pulmonary fibrosis: Identification and validation through transcriptomic and single-cell analyses
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease that has increasingly been associated with dysregulated mitochondrial quality control and dynamics. However, the molecular mechanisms underlying these alterations remain incompletely understood. This study aimed to systematically identify and validate candidate biomarkers related to mitochondrial dynamics in IPF and to characterize their cell-type specificity and putative regulatory relationships. METHODS: We integrated bulk transcriptomic datasets from the Gene Expression Omnibus (GEO), single-cell RNA sequencing (scRNA-seq) data, and literature-derived mitochondrial dynamics gene sets. Candidate genes were identified through differential expression analysis and consensus clustering, followed by functional enrichment and protein-protein interaction (PPI) network analyses. A total of 101 machine-learning model combinations-including random forest, LASSO, and support vector machine-were constructed to select optimal feature genes. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and further evaluated with artificial neural network (ANN) modeling. Additional analyses included chromosomal localization, immune infiltration profiling, multilayer regulatory network construction (transcription factors, lncRNAs, circRNAs), molecular docking prediction, and single-cell expression and pseudotime trajectory analysis. Key biomarkers were further evaluated by RT-qPCR in an independent clinical cohort. RESULTS: Integrated multi-omics and machine-learning analyses identified CD247, IL7R, and RETN as candidate biomarkers related to mitochondrial dynamics-associated pathways in IPF. Across independent transcriptomic datasets, RETN was upregulated, whereas CD247 and IL7R were downregulated, and each showed diagnostic value (single-gene AUC > 0.7). The ANN model based on these genes achieved encouraging discriminative performance (training AUC = 0.91; validation AUC = 0.82), and expression differences were confirmed by RT-qPCR in a modest independent cohort. Enrichment analyses indicated convergence on spliceosome-related pathways, and regulatory-network analysis highlighted interactions involving transcription factors and non-coding RNAs, including circRNA CDR1as. Molecular docking suggested putative interactions with selected compounds. Single-cell analyses suggested that dysregulation was most evident in monocyte-associated compartments in one publicly available scRNA-seq dataset, and pseudotime analysis indicated dynamic expression patterns, with early transient increases in CD247 and IL7R and progressive elevation of RETN. CONCLUSION: Through multi-omics integration and machine-learning approaches, we identified and preliminarily validated CD247, IL7R, and RETN as candidate biomarkers related to mitochondrial dynamics-associated pathways in IPF. These findings provide transcriptomic and cell-type-specific evidence suggesting potential immune-mitochondrial associations in IPF and may inform future biomarker validation and mechanistic hypothesis generation.
26 Apr 2026
Read appraisal →BMJ open
Effects of nurse-led shared decision-making on low-dose CT uptake and screening outcomes in high-risk populations: a systematic review and meta-analysis
OBJECTIVES: To evaluate the effects of nurse-led shared decision-making (SDM) on lung cancer screening outcomes, including low-dose CT (LDCT) uptake, benign findings, early cancer detection and willingness to participate among high-risk populations. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Medline via OvidSP, Cochrane Central Register of Controlled Trials, EMBASE via OvidSP, Web of Science, Scopus, grey literature databases and clinical trial registries were searched from inception to March 2025. ELIGIBILITY CRITERIA: Studies evaluating nurse-led SDM interventions in high-risk lung cancer populations, reporting outcomes including LDCT uptake rates, screening results (Lung-RADS (Lung Imaging Reporting and Data System) classifications), early-stage cancer detection or willingness to participate. Randomised controlled trials, quasi-experimental studies and observational studies were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data and assessed risk of bias using the Risk of Bias in Non-randomised Studies of Interventions (for non-randomised studies) and Cochrane Risk of Bias 2.0 (for randomised controlled trials). Meta-analyses were conducted using random-effects models. Meta-regression explored sources of heterogeneity. RESULTS: 13 studies (n=13 608 participants) were included, comprising 10 single-arm studies and three comparative studies. In single-arm studies without control groups, nurse-led SDM programmes achieved a pooled LDCT uptake rate of 98% (95% CI 28% to 100%; I²=99%), and willingness to participate was 68% (95% CI 24% to 93%; I²=98%). In comparative studies, nurse-led SDM showed no significant difference in LDCT uptake compared with usual care (RR 1.00, 95% CI 0.99 to 1.02; I²=0%), suggesting non-inferiority rather than superiority. Among individuals who completed screening, 81% (95% CI 77% to 85%) had benign or low-risk findings (Lung-RADS [Lung Imaging Reporting and Data System] I/II), and 2% (95% CI 1% to 3%) were diagnosed with early-stage lung cancer, rates consistent with benchmark screening trials. Meta-regression identified female sex as positively associated with uptake (β=0.54, p<0.001), while current tobacco use was negatively associated (β=-0.37, p=0.033). The risk of bias was moderate to serious across studies. CONCLUSIONS: Comparative evidence suggests that nurse-led SDM achieves equivalent LDCT uptake to standard care approaches, indicating feasibility as an alternative service delivery model. However, the predominance of single-arm studies, high heterogeneity and moderate-to-serious risk of bias limit causal inference. High uptake rates in single-arm studies likely reflect selection bias rather than intervention effectiveness. Current evidence supports the feasibility but not the superiority of nurse-led SDM. Well-designed randomised controlled trials are needed to establish comparative effectiveness and cost-effectiveness before recommending widespread integration of nurse-led SDM into lung cancer screening programmes. TRIAL REGISTRATION NUMBER: PROSPERO CRD420251033595. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=1033595.
26 Apr 2026
Read appraisal →The New England journal of medicine
Oral Nirmatrelvir-Ritonavir for Covid-19 in Higher-Risk Outpatients
BACKGROUND: Nirmatrelvir-ritonavir has been shown to reduce progression to severe illness from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in unvaccinated high-risk outpatients. The effectiveness of nirmatrelvir-ritonavir in persons who have been vaccinated, infected naturally, or both is unclear. METHODS: In two open-label platform trials (PANORAMIC in the United Kingdom and CanTreatCOVID in Canada), we enrolled higher-risk adults (≥50 years of age or ≥18 years of age with coexisting conditions) in the community who tested positive for SARS-CoV-2 and had been unwell for 5 days or less. The participants were randomly assigned to receive usual care plus nirmatrelvir (300 mg)-ritonavir (100 mg) twice a day for 5 days or to receive usual care alone. The primary outcome was hospitalization or death from any cause within 28 days after randomization. RESULTS: From December 8, 2021, to September 30, 2024, a total of 3516 participants in the PANORAMIC trial and 716 participants in the CanTreatCOVID trial underwent randomization. In the PANORAMIC trial, 14 of 1698 participants (0.8%) in the nirmatrelvir-ritonavir group and 11 of 1673 participants (0.7%) in the usual-care group were hospitalized or died (adjusted odds ratio, 1.18; 95% Bayesian credible interval, 0.55 to 2.62; probability of superiority, 0.334). In the CanTreatCOVID trial, 2 of 343 participants (0.6%) in the nirmatrelvir-ritonavir group and 4 of 324 participants (1.2%) in the usual-care group were hospitalized or died (adjusted odds ratio, 0.48; 95% Bayesian credible interval, 0.08 to 2.23; probability of superiority, 0.830). In a substudy involving 634 participants, viral load was reduced by the end of treatment with nirmatrelvir-ritonavir. Serious adverse events with nirmatrelvir-ritonavir were reported in 9 participants in the PANORAMIC trial and in 4 participants in the CanTreatCOVID trial. CONCLUSIONS: In two open-label trials, nirmatrelvir-ritonavir did not reduce the incidence of hospitalization or death among vaccinated higher-risk participants with SARS-CoV-2 infection. (Funded by the National Institute for Health and Care Research, and others; PANORAMIC ISRCTN number, 2021-005748-31; CanTreatCOVID ClinicalTrials.gov number, NCT05614349.).
25 Apr 2026
Read appraisal →La Medicina del lavoro
Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses
BACKGROUND: Pneumoconioses remain an important occupational health issue, particularly in low- and middle-income countries. The International Labour Organization (ILO) Classification standardizes chest radiograph interpretation but requires trained readers and is affected by inter-reader variability. This study evaluated whether generative multimodal artificial intelligence (AI) models can approximate ILO-based diagnostic reasoning. METHODS: Eighty-two chest radiographs from the official NIOSH B Reader syllabus were analysed using four AI systems (GPT-4o, GPT-5, MedGemma-4B, MedGemma-27B). Each image was evaluated with a standardized prompt based on the 2022 revised ILO guidelines using deterministic settings. Model outputs were mapped to ILO codes and compared with the official answer keys of the ILO Standard Radiograph Set used for B Reader training and examination. Performance metrics included balanced accuracy, sensitivity, specificity, precision, and Matthews correlation coefficient (MCC). Bootstrap 95% confidence intervals, McNemar's test, and Cohen's κ assessed performance variability and agreement. RESULTS: All four AI models showed moderate diagnostic performance, with balanced accuracy ranging from 60.8% to 70.3%. Sensitivity remained limited (35.5%-54.9%), while specificity was consistently high (84.6%-86.2%). MedGemma-27B performed best for small opacities, GPT-5 for pleural abnormalities and for technical quality. Large opacities and rare findings were systematically under-detected. Statistical comparisons showed significant differences between models, although agreement patterns were broadly similar. CONCLUSION: All AI models partially followed structured ILO radiographic criteria but did not achieve expert-level performance, confirming that they cannot replace certified B Readers. Larger, real-world datasets are needed to assess their potential clinical utility as supportive tools in occupational health surveillance programs.
25 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Predictive biomarkers of placebo response in patients with cancer: an exploratory analysis of the ABCD randomized clinical trial
PURPOSE: Placebo effect is commonly observed in symptom management randomized clinical trials. However, there are currently no established predictive biomarkers of placebo response. We examined the patterns of association between placebo response and baseline plasma cytokine levels for 10 symptoms in patients with cancer. METHODS: This is an exploratory analysis of the double-blind, randomized ABCD trial, which compared high-dose dexamethasone to placebo for dyspnea treatment in patients with cancer (clinicaltrials.gov; NCT03367156). The primary outcome was change in dyspnea intensity. Four plasma cytokines (TNF, IL-6, IL-8, and IL-10) and 10 Edmonton Symptom Assessment System symptoms were measured at baseline, day 7, and day 14. Generalized additive models and exploratory analyses were used to examine the patterns of association between baseline cytokine levels and symptom responses on day 7 and day 14 in placebo and dexamethasone groups. RESULTS: This analysis included data from 45 patients. In the placebo group, we observed a significant negative association (p < 0.05) between ≥ 1 of the measured cytokines' baseline levels across multiple symptoms on day 7 (5/10 symptoms) and day 14 (3/10 symptoms), including appetite, drowsiness, nausea, and pain. Exploratory analysis revealed a negative association in the placebo group in 33/40 cytokine-symptom response combinations for day 7 (p < 0.001) and 29/40 combinations for day 14 (p = 0.006). CONCLUSION: Our preliminary findings suggest patients with high baseline cytokine levels may be less likely to report placebo response for multiple symptoms. Upon further confirmation, these findings may have implications for future clinical trial design. In this exploratory analysis, we observed that patients with cancer were less likely to report placebo response for multiple symptoms if they had higher levels of baseline cytokines. These preliminary findings have important potential implications for placebo-controlled clinical trials, including patient selection, design of interventional controls, and trial interpretation. Additionally, they invite further research on possible mechanistic links between inflammation and placebo effect.
25 Apr 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Health Hazards of E-Cigarettes and Heated Tobacco Products: a Comprehensive Analysis of Hazardous Substances and Regulatory Gaps
INTRODUCTION: Ingredients and products of electronic cigarettes (ECs) and heated tobacco products (HTPs) formed during heating are aerosolized and inhaled. Hazard information on ingredients and emissions is often unavailable. Our objective was to identify and classify substances in the ingredients and emissions of ECs and HTPs based on scientific hazard classifications. METHODS: Ingredient data were collected from the European Union (EU) Common Entry Gate, a mandatory notification system for tobacco and related products. Substances in ECs and HTPs emissions were identified with a systematic literature review and classified into three categories based on existing health hazard classifications. Category 1 substances (i.e., highest potential health hazard) were regarded as high-priority substances for research and quantification. Categories 2 and 3 included potentially harmful substances and substances lacking sufficient toxicological data respectively. RESULTS: A total of 134 of the 1740 identified substances were classified in Category 1. Several substances possess Carcinogenic, Mutagenic, or Reprotoxic properties, prohibited as tobacco additives. Most substances lacked sufficient toxicity data and were classified in Category 3. CONCLUSION: Our classification can be used for monitoring and enforcement, ensuring proactive management of potential risks. Therefore, we recommend strengthening the enforcement of existing bans. Implications: This study identifies and classifies 1740 substances found in the ingredients and emissions of ECs and HTPs, including 134 hazardous chemicals, among which are carcinogens, mutagens, and reproduction toxicants. This indicates regulatory gaps and the need for stricter enforcement of existing bans. The findings provide a valuable tool for policymakers and public health professionals for monitoring and mitigating risks.
24 Apr 2026
Read appraisal →The New England journal of medicine
Ketamine or Etomidate for Tracheal Intubation of Critically Ill Adults
BACKGROUND: For critically ill adults undergoing tracheal intubation, observational studies suggest that the use of etomidate to induce anesthesia may increase the risk of death. Whether the use of ketamine rather than etomidate decreases the risk of death is uncertain. METHODS: In a randomized trial conducted in 14 emergency departments and intensive care units in the United States, we randomly assigned critically ill adults who were undergoing tracheal intubation to receive ketamine or etomidate for the induction of anesthesia. The primary outcome was in-hospital death from any cause by day 28. The secondary outcome was cardiovascular collapse during intubation, defined by the occurrence of a systolic blood pressure below 65 mm Hg, receipt of a new or increased dose of vasopressors, or cardiac arrest. RESULTS: A total of 2365 patients underwent randomization and were included in the trial population; 1176 were assigned to the ketamine group and 1189 to the etomidate group. In-hospital death by day 28 occurred in 330 of 1173 patients (28.1%) in the ketamine group and in 345 of 1186 patients (29.1%) in the etomidate group (risk difference adjusted for trial site, -0.8 percentage points; 95% confidence interval [CI], -4.5 to 2.9; P = 0.65). Cardiovascular collapse during intubation occurred in 260 of 1176 patients (22.1%) in the ketamine group and in 202 of 1189 patients (17.0%) in the etomidate group (risk difference, 5.1 percentage points; 95% CI, 1.9 to 8.3). Prespecified safety outcomes were similar in the two groups. CONCLUSIONS: Among critically ill adults undergoing tracheal intubation, the use of ketamine to induce anesthesia did not result in a significantly lower incidence of in-hospital death by day 28 than etomidate. (Funded by the Patient-Centered Outcomes Research Institute and others; RSI ClinicalTrials.gov number, NCT05277896.).
24 Apr 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
Health-related quality of life in patients with obstructive sleep apnea - a systematic review update assessing the quality of measurement properties of patient-reported outcome measures
PURPOSE: The purpose of this systematic review update was to identify existing health-related quality of life (HRQOL) patient-reported outcome measures (PROMs) for obstructive sleep apnea (OSA). Building upon a 2016 systematic review, which recommended four PROMs with the highest evidence, this investigation was warranted to synthesize new evidence and evaluate new PROMs that have emerged since then via the Consensus based Standards for the Selection of Health Measurement Instruments (COSMIN). METHODS: We searched Medline via PubMed, Embase and the Cochrane Library for HRQOL PROM development and validation studies published in English or German. Studies focusing on cross-cultural validation, those in which PROMs were assessed as secondary outcomes, and studies limited to specific demographic subgroups or single HRQOL domains were excluded. For risk of bias and quality of measurement properties (MP) assessment, we used the COSMIN methodology to facilitate comparability with the previous review, as well as the GRADE approach for evidence level evaluation. RESULTS: We identified two new PROMs, the Patient-Reported Apnea Questionnaire (PRAQ) and the Symptoms, Tiredness, Alertness, Mood and Psychosocial instrument (STAMP), which were validated in two studies and a single study, respectively. Overall, the PRAQ displays an moderate risk of bias with excellent quality of MP and high evidence for content validity in particular, whereas the STAMP shows a higher risk of bias and low quality of MP. CONCLUSION: The PRAQ has demonstrated the strongest evidence among PROMs since 2016. In total, including the previous review, five PROMs are currently recommended for assessing HRQOL in individuals with OSA. REVIEW REGISTRATION: The review was registered with PROSPERO under the registration number CRD42024602020.
24 Apr 2026
Read appraisal →BMJ quality & safety
Sustainability of the de-implementation of low-value care in infants with bronchiolitis: 2-year follow-up of a cluster randomised controlled trial
BACKGROUND: In 2017, the PREDICT (Paediatric Research in Emergency Departments International Collaborative) network conducted a cluster randomised controlled trial (cRCT) at 26 Australian and New Zealand hospitals to improve bronchiolitis care. Findings demonstrated that targeted interventions significantly improved adherence with five evidence-based low-value bronchiolitis practices (no chest radiography, salbutamol, glucocorticoids, antibiotics and epinephrine) in the first 24 hours of hospitalisation (adjusted risk difference, 14.1%; 95% CI: 6.5% to 21.7%; p<0.001). During the intervention year (2017), intervention hospital (n=13) compliance was 85.1% (95% CI: 82.6% to 89.7%). This study aimed to determine if improvements in bronchiolitis management were sustained at intervention hospitals 2 years post-trial completion. METHODS: International, multicentre follow-up study of hospitals in Australia and New Zealand that participated in a cRCT of de-implementation of low-value bronchiolitis practices, 1 year (2018) and 2 years (2019) post-trial completion, obtained retrospectively from medical audits. Sustainability was defined a priori as no more than a <7% decrease to any level of improvement in adherence for all five low-value practices (composite outcome) from the cRCT intervention year. RESULTS: Of the 26 hospitals, 11 intervention and 10 control hospitals agreed to participate in the follow-up study. Data were collected on 3299 infants with bronchiolitis 1 year (intervention and control hospitals) and 1689 infants 2 years post-trial (intervention hospitals). Adherence with no use of the five low-value practices 2 years post-trial completion was 80.9% (adjusted predicted adherence, 80.8%, 95% CI: 77.4% to 84.2%; estimated risk difference from cRCT outcome -3.9%, 95% CI: -8.6% to 0.8%) at intervention hospitals, fulfilling the a priori definition of sustainability. DISCUSSION: Targeted interventions, delivered over one bronchiolitis season, resulted in sustained improvements in bronchiolitis management in infants 2 years later. This follow-up study provides evidence for sustainability in de-implementing low-value care in bronchiolitis management. TRIAL REGISTRATION DETAILS: Australian and New Zealand Clinical Trials Registry No: ACTRN12621001287820.
23 Apr 2026
Read appraisal →JMIR formative research
Effects of a Modified Exposure Claim for an e-Cigarette on Claim Comprehension, Behavioral Intentions, and Risk Perceptions Among US Adult Tobacco Users and Nonusers: Randomized Experimental Study
BACKGROUND: Effective communication about the relative risks of cigarettes and e-cigarettes can help increase switching away from cigarettes while minimizing unintended use. OBJECTIVE: This study examined comprehension of a proposed modified exposure claim (MEC) about an e-cigarette (IQOS VEEV, the study product [SP]) and the effects of claim exposure on SP use intentions and risk perceptions among adult tobacco users and nonusers. METHODS: Adult smokers with no intention to quit smoking (S-NIQ, n=606), adult smokers with an intention to quit smoking (S-IQ, n=600), adult e-cigarette users (ECU, n=630), adult former smokers (FS, n=619), adult tobacco and nicotine products (TNP) never-users aged 18-24 years (n=648), and adult TNP never-users aged 25 years and older (n=749; total N=3852) participated in a randomized between-groups online experimental study. Participants viewed a marketing brochure for the SP with (test condition) or without (control condition) an embedded MEC. Outcome measures included claim comprehension, intention to use the SP regularly, and perceived health risk to self from using the SP or smoking cigarettes. RESULTS: Most participants were female (n=2110, 54.8%), had a mean age of 40.2 (SD 14.93) years, and were equally split across the 4 US regions. S-IQ and S-NIQ were long-term, frequent cigarette smokers, while 91.4% (566/619) of FS were long-term quitters. ECU on average used e-cigarettes ≥15.2 times per day, and the majority of them (552/630, 87.6%) had started using e-cigarettes more than 12 months before. Most participants correctly understood the key elements of the claim: the SP produces lower levels of harmful chemicals compared to cigarettes (1818/1926, 94.4%), and switching completely from cigarettes to the SP reduces exposure to harmful chemicals (1832/1926, 95.1%). In both conditions, positive intention to use the SP was high among ECU (control: 238/314, 75.8% vs test: 249/315, 79%; P=.33), moderate among S-IQ (control: 127/299, 42.5%; test: 166/299, 55.5%; P<.001) and S-NIQ (control: 140/299, 46.8%; test: 166/307, 54.1%; P=.07), low among FS (control: 28/306, 9.2%; test: 33/312, 10.6%; P=.55), and very low among adult TNP never-users aged 18-24 years (control: 3/330, 0.9%; test: 8/318, 2.5%; P=.11), and adult TNP never-users aged 25 years and older (control: 5/373, 1.3% vs test: 12/375, 3.2%; P=.09). All groups understood that the SP posed a lower health risk compared to cigarettes. In all groups, claim exposure was associated with significantly lower risk perception of the SP relative to cigarettes (all comparisons, P<.001). CONCLUSIONS: The tested MEC has the potential to benefit public health by simultaneously increasing already high levels of SP use intention and reducing SP risk perceptions relative to cigarettes among adult tobacco users while generating low levels of use intention among tobacco nonusers.
23 Apr 2026
Read appraisal →The Cochrane database of systematic reviews
Lung transplant in people with cystic fibrosis and nontuberculous mycobacteria infection
RATIONALE: Cystic fibrosis (CF) is an inherited multi-organ disorder. People with CF (pwCF) experience recurrent and chronic lung infections and a progressive loss of lung function. PwCF with poor and rapidly declining lung function may be considered for lung transplantation (LTx), which may improve their quality of life and survival. Nontuberculous mycobacteria (NTM) can cause pulmonary disease in pwCF, and NTM infection is a poor prognostic factor in LTx. Guidelines recommend NTM infection should not automatically preclude LTx. It is important to evaluate the evidence base for LTx in pwCF and NTM pulmonary disease. OBJECTIVES: To evaluate clinical outcomes in pwCF and with NTM infection (NTM infection alone or with NTM pulmonary disease) who undergo LTx by comparing: 1. pwCF with current NTM lung infection who undergo LTx versus those with NTM infection who do not undergo LTX; 2. pwCF with current NTM lung infection who undergo LTx versus those without NTM undergoing LTx. SEARCH METHODS: We searched the Cochrane Cystic Fibrosis Trials Register, CENTRAL, MEDLINE, Embase, and PubMed as well as two ongoing trials registries. We checked references. The latest search date was 17 February 2026. ELIGIBILITY CRITERIA: We considered non-randomised studies of pwCF (any age) with or without NTM lung infection or disease being considered for LTx as well as studies of pwCF and NTM who either did or did not undergo LTx. OUTCOMES: Our critical outcomes were mortality, disseminated NTM infection post-LTx, time to chronic lung allograft dysfunction (CLAD), and quality of life at any time points reported. We additionally planned to report lung function, hospitalisations for pulmonary exacerbations, and nutritional parameters in the review. RISK OF BIAS: We assessed the risk of bias in three studies using ROBINS-I and in one study using the Joanna Briggs Institute checklist for case series. SYNTHESIS METHODS: We could only report results narratively. We used GRADE to assess the certainty of the evidence. INCLUDED STUDIES: We included four single-centre retrospective studies (388 adults). Sample sizes ranged from nine to 177 participants. Mycobacteria abscessus was the most common NTM species identified, and all studies reported infection with other pathogens. All studies compared pwCF and NTM infection to pwCF without NTM infection, all undergoing LTx. Each study reported mortality and disseminated NTM infection post-LTx; two studies recorded CLAD. No study reported quality of life, specific lung function measures (although one study commented briefly on lung function in general), hospitalisations for pulmonary exacerbations, or nutritional parameters. SYNTHESIS OF RESULTS: We analysed all NTM infections together for practical reasons and were not able to undertake a planned subgroup analysis by subspecies, but acknowledge that the prognosis and clinical trajectory of pwCF infected with different NTM may not be similar. We downgraded the certainty of the evidence due to non-randomised study design and serious risk of bias across all studies. We assessed all identified evidence as of very low certainty, such that lung transplant may have little to no effect on any of the outcomes listed below, but the evidence is very uncertain. Mortality Two studies (18 participants with NTM) reported similar survival data between NTM-positive LTx recipients and matched controls without NTM. Another study (9 participants) reported that two of five participants NTM-positive at LTx died within a few months post-LTx, whilst one of four NTM-negative participants died three years post-LTx due to chronic rejection. One study (177 participants) found that pwCF who had positive NTM cultures pre-LTx had a longer median survival duration than those who had negative cultures. This study additionally reported on survival of participants with post-LTx NTM infection, finding that the five participants who had post-LTx NTM disease had a longer mean survival duration than the 141 participants without post-LTx NTM disease. Disseminated NTM infection post-LTx In the largest study, of the 18 pwCF with NTM at the time of LTx, seven had at least one positive NTM culture, and four developed NTM disease post-LTx. Conversely, 79 of the 89 pwCF without NTM remained so post-LTx; 10 participants recorded a positive NTM culture, but none developed NTM disease. For the 39 participants without a baseline NTM culture, three participants recorded positive NTM cultures post-LTx, and one developed NTM disease. Of the remaining small studies, one reported that NTM was isolated in four of 13 participants at LTx and in three of these post-LTx. A second study reported that one out of five pwCF had NTM infection post-LTx (all were positive at LTx). The third study reported that five out of nine participants had NTM disease at LTx, and two of these five remained NTM-positive post-LTx. CLAD Two studies assessed CLAD. One study reported that none of the five NTM-positive LTx recipients developed CLAD, stating that the risk of CLAD appeared to be similar between the NTM and the comparator group. The second study stated that three out of nine LTx recipients with NTM disease developed chronic rejection or graft dysfunction. AUTHORS' CONCLUSIONS: There are no randomised trials to guide clinicians and patients or their families when making decisions regarding LTx in pwCF with NTM. The available data come from observational studies and registry data, often with few people with NTM reported. It has not been possible to pool the available data in meta-analysis, and we are very uncertain of the effect of NTM on pwCF undergoing LTx on the risk of developing NTM disease post-LTx, survival after LTx, and the development of CLAD. The studies were small and at times contradictory. In the era of highly effective modulator treatments, as some centres do not offer LTx to people with a history of NTM, there is an urgent need for more data to guide decision-making. FUNDING: This review is part of a suite of reviews on NTM funded jointly by the CF Foundation and the CF Trust. REGISTRATION: Protocol registration (2024): www.crd.york.ac.uk/PROSPERO/view/562682.
23 Apr 2026
Read appraisal →Journal of rehabilitation medicine
Effect of inspiratory muscle training on inspiratory muscle strength in adults with post-COVID-19 condition and inspiratory muscle weakness: a randomized controlled trial
OBJECTIVE: Evaluate the effect of inspiratory muscle training in adults with post-COVID-19 condition (PCC) and inspiratory muscle weakness. DESIGN: Randomized controlled trial. SUBJECTS/PATIENTS: Adults with PCC and inspiratory muscle weakness. METHODS: Participants were randomized to inspiratory muscle training twice daily plus individualized exercise twice weekly or exercise alone, with weekly follow-ups over 8 weeks. PRIMARY OUTCOME: inspiratory muscle strength (Maximal Inspiratory Pressure). SECONDARY OUTCOMES: expiratory muscle strength (Maximal Expiratory Pressure); functional capacity (Six‑Minute Walk Test; One‑Minute Sit‑to‑Stand); lung function (spirometry); dyspnoea (mMRC); respiratory symptoms (chest tightness, impaired deep breathing, breathing‑related pain); cough frequency (CAAT cough item); fatigue (Fatigue Severity Scale); physical activity (Frändin-Grimby Activity Scale); activity limitations (Patient‑Specific Functional Scale); and health-related quality of life (EQ‑5D‑5L). Intention-to-treat analyses used imputed missing data. Estimated sample size: 90. RESULTS: Forty-four participants were included (median age 47; 82% women; n = 22/group). Between‑group differences favoured the intervention for inspiratory muscle strength (mean difference: 18%; 95% CI: 5-30; OR for clinically meaningful improvement: 7.08, 95% CI: 1.31-38.32) and cough frequency. No other between-group differences were observed. CONCLUSION: Inspiratory muscle training may improve inspiratory muscle strength and reduce cough frequency, but limited sample size and underrepresentation of the most severely affected warrant cautious interpretation.
23 Apr 2026
Read appraisal →European journal of pediatrics
Different doses of vitamin A supplementation as adjuvant treatment for pneumonia in children: a network meta-analysis of randomized controlled trials
UNLABELLED: While previous meta-analyses have established a link between vitamin A supplementation and outcomes in children with pneumonia, the efficacy and safety of different dosing regimens remain unclear. Existing evidence is predominantly based on single-dose trials, creating a critical knowledge gap. We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang and VIP databases from their inception to December 2025. Study searches were independently carried out in December 2025 by two reviewers. Two researchers used the Cochrane risk of bias tool to independently assess the quality of the included studies. Data analysis was conducted through the "netmeta" package in R software (version 4.3.3). Twenty-five trials involving 3116 patients were included. Network meta-analysis revealed that medium-dose vitamin A supplementation was most effective in reducing the duration of fever and cough. Low-to-medium doses were consistently associated with better clinical outcomes than high-dose regimens. However, evidence regarding safety outcomes remained limited and inconclusive. CONCLUSION: Low-to-medium dose vitamin A supplementation appears superior to high-dose regimens for alleviating clinical symptoms in children with pneumonia. However, the evidence is limited by high heterogeneity, indirect comparisons between dose groups, and poor reporting of adverse events. Future trials should prioritize direct dose-comparisons and rigorous safety monitoring to refine dosing guidelines. WHAT IS KNOWN: • Pneumonia poses a significant threat to the health and lives of children. Vitamin A supplementation plays an adjuvant role in the treatment of pneumonia in children. • There are currently divergent conclusions in the research regarding the optimal dosage of vitamin A, particularly with regard to the safety of high-dose supplementation and the effectiveness of low-dose supplementation. WHAT IS NEW: • This study revealed that medium-to-low-dose vitamin A supplementation appears to be more effective than high-dose therapy in alleviating the primary symptoms of childhood pneumonia. • This study also suggests that children in developed regions have higher baseline serum retinol levels, which may diminish the marginal benefits of high-dose supplementation and may even predispose them to its immunosuppressive effects.
22 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Nivolumab ± Ipilimumab in Patients With Pretreated Advanced Neuroendocrine Carcinoma: The GCO-001 NIPINEC Randomized Phase II Trial
PURPOSE: There is no standard second-line therapy for gastroenteropancreatic (GEP) and lung large-cell neuroendocrine carcinoma (NEC) after the failure of platinum-based chemotherapy. This study aimed to investigate the efficacy of nivolumab ± ipilimumab. METHODS: The GCO-001-NIPINEC (ClinicalTrials.gov identifier: NCT03591731) trial was a noncomparative, open-label, phase II trial. The main inclusion criteria were age ≥18 years, performance status (PS) ≤2, advanced large- and small-cell GEP-NEC and large-cell lung NEC, and second- or third-line treatment for NECs refractory to platinum-based chemotherapy. Patients were randomly assigned (1:1) and stratified by age and PS to receive nivolumab (3 mg/kg/once every 2 weeks) ± ipilimumab (1 mg/kg/once every 6 weeks) for 2 years or until progression or unacceptable toxicity. The primary end point was objective response rate (ORR) at 8 weeks, assessed by investigators. RESULTS: A total of 185 patients (91 in the nivolumab arm and 94 in the nivolumab-ipilimumab arm) were enrolled between December 2018 and March 2021; 169 were analyzed (median age of 64.5 years, 71% male, 91% PS 0-1). The main primary tumor locations were lungs (50%), colorectal (15%), gastroesophageal (14%), and pancreatic (13%) regions. The ORR at 8 weeks was 7.2% (95% CI, 2.7 to 15.1]) in the nivolumab arm and 14.0% (95% CI, 7.4 to 23.1) in the nivolumab-ipilimumab arm. The best ORR was 9.6% and 20.9%, respectively, whereas the median progression-free and overall survival were approximately 2 months and 6 months in both arms. One treatment-related death occurred, in the nivolumab arm. The grade 3-4 adverse events (≥5%) were asthenia (13%), gamma-glutamyl transferase increase (10%), alkaline phosphatase increase (9%), dyspnea (7%), and anemia (6%) in the nivolumab-ipilimumab arm. CONCLUSION: Nivolumab-ipilimumab could be a second-/third-line treatment option for patients with NECs. However, given the limited magnitude of benefit, studies are warranted to evaluate its use earlier and/or associated with chemotherapy.
21 Apr 2026
Read appraisal →Medicine
Saturation effect of anion gap in predicting long-term mortality in critically ill patients with chronic obstructive pulmonary disease (COPD): A retrospective cohort study based on the MIMIC-IV database
The relationship between anion gap (AG) and long-term mortality in intensive care unit has been widely reported, but whether this association exists in critically ill patients with chronic obstructive pulmonary disease (COPD) is still unknown. The data of this study were collected from the Medical Information Mart for Intensive Care-IV. First of all, we used the Cox regression analysis and Kaplan-Meier curves to measure the relationship between AG and 365-day mortality for critically ill patients with COPD. Next, a restricted cubic spline was used to analyze the relationship between AG and mortality. Finally, age, sex, weight, hypertension, type 2 diabetes mellitus, heart failure, myocardial infarction, and chronic kidney disease were considered for subgroup analysis. A total of 2594 eligible subjects were sampled, of which 36.24% died within 365 days of intensive care unit admission. Cox regression analysis, after adjusting for confounders, demonstrated a significant association between AG and 365-day mortality in patients with COPD (hazard ratio = 1.03, 95% confidence interval: 1.01-1.05. P < .05). Stratifying AG into quartiles revealed higher levels of AG associated with an increased risk of death (Q1: 1.00, Q2: 1.34 [1.09-1.66], Q3: 1.44 [1.17-1.78], and Q4: 1.49 [1.19-1.87]). Additionally, restricted cubic spline analysis indicated a nonlinear relationship, with a critical value of AG at 14 mmol/L. Subgroup analysis highlighted AG as a significant predictor of long-term mortality in COPD patients across different subgroups, with an interaction effect observed in the subgroup with type 2 diabetes mellitus. In critically ill patients with COPD, there was a significant positive association between AG and 365-day mortality. In addition, there is a saturation effect at AG of 14 mmol/L.
20 Apr 2026
Read appraisal →Archives of disease in childhood. Fetal and neonatal edition
Duration of apnoea before start of backup ventilation during nCPAP in extremely preterm infants and time spent within the SpO2 target: a randomised cross-over study
OBJECTIVE: Central apnoea due to immaturity of the respiratory drive constitutes the main cause of frequent and prolonged desaturations in extremely preterm (EPT) infants <28 weeks. We investigated the impact of varying the duration of apnoea before backup ventilation (BUV) on the measures of oxygenation in EPT infants during nasal continuous positive airway pressure (nCPAP) therapy. DESIGN: Single-centre randomised cross-over trial. SETTING: Level 3 neonatal intensive care unit. PATIENTS: 24 EPT infants on nCPAP with BUV. MAIN OUTCOME MEASURES: The primary outcome was the time spent within a predefined oxygen saturation (SpO2) target (88%-95% or ≥88% with fraction of inspired oxygen (FiO2) =0.21) during start of BUV after 4 s of apnoea duration (AD 4) or 16 s of apnoea duration (AD 16) RESULTS: The study was successfully completed in 22 children (median gestational age 24+5 weeks, birth weight 628 g, postnatal age 48 days). Mean time spent within the SpO2 target didn't differ between AD 4 and AD 16 (66.9% vs 67.2%, p=0.88). There were no differences in the time below or above the SpO2 target, prolonged (>30 s, >60 s, >120 s) and severe (<80%, <70%) episodes of hypoxaemias and cerebral tissue oxygenation. Mean FiO2, mean airway pressure, transcutaneous carbon dioxide pressure, heart rate and respiratory frequency did not differ while the rate of BUV was significantly higher during AD 4. CONCLUSION: Reducing the time of apnoea until start of BUV didn't improve the time spent within the SpO2 target in respiratory unstable EPT infants. Our data demand intensified efforts to specify these settings of non-invasive respiratory support that better achieve this important clinical goal. TRIAL REGISTRATION NUMBER: DRKS00031911.
20 Apr 2026
Read appraisal →Medicine
Trends and determinants of tuberculosis incidence in Turkey: A secondary data analysis
Tuberculosis remains a major public health concern, with incidence rates influenced by social determinants of health. Although tuberculosis incidence in Turkey has declined markedly in recent decades, the factors associated with this decline have not been comprehensively evaluated at the country-level. This study aimed to examine trends in tuberculosis incidence in Turkey from 1982 to 2021 and to identify determinants of tuberculosis incidence rates between 2000 and 2021, considering variables categorized under composite development, economic indicators, population, and health services. A longitudinal ecological study was conducted using secondary data to examine trends and determinants of tuberculosis incidence in Turkey. Univariate and multivariable linear regression were employed to evaluate the associations between tuberculosis incidence rates and 10 selected variables. Tuberculosis incidence in Turkey declined substantially over the 40-year period analyzed, with an overall decrease of 86.6% and sharper reductions observed in certain intervals. In multivariable linear regression, only the Human Development Index remained independently associated with tuberculosis incidence (β = -1.041, P < .001), indicating that higher Human Development Index values were associated with lower tuberculosis incidence rates in Turkey. These findings indicate that tuberculosis incidence in Turkey has declined alongside changes in human development, as reflected by the composite Human Development Index.
20 Apr 2026
Read appraisal →BMJ open respiratory research
Comparing paper Letters in addition to Emailed Audit and feedback in Refining Asthma treatment to Improve clinical and environmental Results in primary care through a cluster randomised controlled trial: the CLEAR AIR study
BACKGROUND: Suboptimal use of preventer inhalers and salbutamol reliever overprescribing are associated with preventable asthma deaths and are a major source of primary care carbon emissions. Audit and feedback produces modest behaviour change by assessing clinical performance and delivering feedback to encourage improvement. Although feedback is increasingly delivered digitally, clinicians may respond more to additional printed feedback reports. We evaluated whether combined digital and paper feedback was more effective than digital-only feedback in promoting safer and greener asthma prescribing at the practice level. METHODS: In this parallel, cluster randomised controlled trial, all 273 primary care practices in West Yorkshire were assigned within their primary care network clusters by stratified, permuted block randomisation to receive seven bimonthly reports on asthma prescribing either in 'digital and paper' (intervention) or 'digital-only' (control) formats. The primary outcome was the proportion of preventer inhalers prescribed in pressurised metred-dose devices due to their high carbon footprint. Intervention group allocation was concealed. The intention-to-treat population was analysed and adjusted for both potential confounders and preintervention achievement. RESULTS: Final analysis assessed 270 practices in 26 clusters per arm due to practice mergers within the control group. There was no significant difference between the intervention groups based on change in the primary outcome (intervention-0.15%; control-0.19%; risk ratio-1.00; 95% CI 0.98 to 1.03) nor any secondary outcome. Analysis of both interventions combined showed a background trend of mixed improvement following feedback. CONCLUSIONS: There was no evidence that combined paper and digital feedback was more effective than digital-only feedback, despite the background of mixed improvements following both interventions. Challenges remain to understanding the barriers to influencing the prescribing of preventer inhalers and transitioning inhaler devices towards low-carbon 'green' alternatives; however, this study demonstrated the value of an efficient 'real-world' trial embedded within an existing quality improvement initiative. TRIAL REGISTRATION NUMBER: NCT05761873.
20 Apr 2026
Read appraisal →PloS one
Hybrid feature-selection and diversity-guided stacking framework for interpretable ensemble learning: Application to COVID-19 mortality prediction
BACKGROUND: Reliable predictive modeling in high-dimensional biomedical data requires a balance between accuracy, interpretability, and computational efficiency. However, existing ensemble methods often overlook model diversity or rely on ad hoc feature-selection approaches, which limit generalizability. This study introduces a hybrid feature-selection and diversity-guided stacking framework designed to improve robustness and scalability across clinical and other data-intensive domains. METHODS: The proposed framework integrates a hybrid feature-selection pipeline-combining Variance Inflation Factor (VIF), Analysis of Variance (ANOVA), Sequential Backward Elimination (SBE), and Lasso regression-to reduce multicollinearity and overfitting. It also employs a diversity-aware stacking strategy that constructs sub-model sets based on pairwise diversity measures (Disagreement, Yule's Q, and Cohen's Kappa) and non-pairwise metrics (Entropy and Kohavi-Wolpert). Sixteen base classifiers and five meta-learners were trained using repeated 10-fold cross-validation. The framework was evaluated using data from 4,778 hospitalized COVID-19 patients with 116 clinical and laboratory attributes, preprocessed using robust scaling and ROSE-based class balancing. RESULTS: The optimal configuration, which stacked Random Forest and XGBoost models using a Neural Network meta-learner, achieved 91.4% accuracy (95% CI: 89.8-92.8), AUC = 0.955, F1 = 0.801, and MCC = 0.746, outperforming the best individual model (AdaBoost, 90.2%). Training time (~450 s) and per-case inference time (<0.2 s) demonstrated computational feasibility. Feature-importance analysis and SHAP-based interpretation confirmed clinical relevance and interpretability. CONCLUSIONS: The hybrid feature-selection and diversity-guided stacking framework improves predictive accuracy and interpretability while maintaining computational efficiency. Although validated using COVID-19 mortality data, the approach is broadly applicable to biomedical, environmental, and engineering prediction tasks that require interpretable and scalable ensemble learning.
19 Apr 2026
Read appraisal →Sleep
Optimizing automated sleep stage scoring of 5-s mini-epochs: a transfer learning study
STUDY OBJECTIVES: Conventional sleep staging relies on 30-s epochs, potentially concealing transient sleep stage intrusion and reducing precision. Building on our previous study of mini-epochs, we investigated whether U-Sleep, an existing automatic deep learning-based sleep staging model with high performance in epochs, could be optimized to similar performance level in 5-s mini-epoch scoring, thereby enabling more detailed sleep characterization. METHODS: We created a dataset of 48 000 human-scored 5-s mini-epochs from 100 polysomnographies. We compared mini-epochs to human-scored epochs before U-Sleep was optimized using transfer learning and evaluated on a test set. Model performance was assessed using F1-scores, confusion matrices, stage distributions and transition rates comparing scorings of the original U-Sleep before, and the optimized U-Sleep after transfer learning to human-scored mini-epochs. RESULTS: Compared to human-scored epochs, human-scored mini-epochs captured significantly more transitions (1.70/min vs. 0.21/min, p < .001), and significantly more wake (8.4 per cent vs. 5.4 per cent), N1 (7.2 per cent vs. 5.4 per cent), and N2 (51.8 per cent vs. 40.9 per cent), less N3 (15.4 per cent vs. 25.2 per cent), and REM sleep (16.7 per cent vs. 23.0 per cent) (all p < .001). Optimizing U-Sleep improved its performance significantly from F1 = 0.74 to F1 = 0.81 (p < .05) and gave increased transition rates in the test set (original U-Sleep: 1.06/min, optimized U-Sleep: 1.34/min, human-scored mini-epochs: 1.70/min). Stage distributions did not differ between optimized U-Sleep's scorings and human-scored mini-epochs. CONCLUSION: After optimization, U-Sleep performance in mini-epochs matched the high performance levels previously reported in both human and automated 30-s epoch scoring. This demonstrates the feasibility of precise, automated high-resolution sleep staging. Future work should include external validation and application to full-night recordings. Statement of Significance Conventional 30-s epochs limit temporal resolution in sleep staging and may conceal transient intrusions of wake or sleep stages. However, no validated methods are available for high-resolution scoring. In this study, we trained and validated the state-of-the-art deep learning model U-Sleep for accurate automatic 5-s mini-epoch scoring using a large dataset of human-scored mini-epochs. The optimized model achieved a high performance, matching levels from previously reported automatic and human epoch scoring. Compared to epoch scoring, mini-epochs captured significantly more stage transitions, supporting their ability to uncover sleep dynamics that are otherwise lost. Our findings show the potential of high-resolution sleep staging for more detailed characterization of sleep architecture and demonstrate the feasibility of precise, automatic mini-epoch scoring.
19 Apr 2026
Read appraisal →Archives of disease in childhood. Fetal and neonatal edition
Closed-loop automated oxygen control in preterm ventilated infants: a randomised controlled trial
OBJECTIVE: To compare the duration of mechanical ventilation between preterm infants receiving closed-loop automated oxygen control (CLAC) or manual oxygen control. DESIGN: Randomised controlled trial. SETTING: Tertiary neonatal unit in London, UK. PATIENTS: Infants (n=69) with a median (IQR) gestational age of 27.0 (25.6-29.0) weeks studied at a corrected postmenstrual age of 27.6 (25.9-29.1) weeks. INTERVENTIONS: Infants were randomised to CLAC or manual oxygen control within 48 hours of initiation of mechanical ventilation if less than 7 days of age until successful extubation. MAIN OUTCOME MEASURES: Duration of mechanical ventilation. RESULTS: The CLAC infants (n=34) compared with those who received manual control had a shorter duration of mechanical ventilation (median (range): 11 (1-57) vs 40 (3-134) days, p=0.027), a shorter duration of supplemental oxygen (median (range): 33 (0-100) vs 47 (3-335) days, p=0.031), a lower incidence of bronchopulmonary dysplasia (BPD) at 36 weeks postmenstrual age (55% vs 83.9%, p=0.015) and fewer required home oxygen (26.5% vs 51.4%, p=0.016). In the CLAC infants, the time spent in the target oxygen range (91%-95%) was increased (p<0.001) and the times spent in hypoxaemia (peripheral oxygen saturation level (SpO2)<85%) and hyperoxaemia (SpO2>95%) were reduced (p<0.001). CONCLUSIONS: Use of CLAC in preterm, ventilated infants was associated with improved achievement of oxygen saturation targets, shorter durations of mechanical ventilation and supplemental oxygen treatment and a lower incidence of BPD. These results need to be replicated in larger multicentre studies before any change in routine practice could be recommended. TRIAL REGISTRATION NUMBER: NCT05030337.
19 Apr 2026
Read appraisal →Archives of disease in childhood. Fetal and neonatal edition
Intermittent hypoxia and caffeine in infants born preterm: the ICAF Randomized Clinical Trial
OBJECTIVE: To determine whether extending caffeine therapy through 43 weeks' postmenstrual age (PMA) decreases intermittent hypoxia (IH) in convalescing preterm infants. Secondary objectives were to assess caffeine effects on changes in inflammation-related plasma biomarkers and brain MRI. DESIGN: Multicentre masked randomised trial. SETTING: 16 US hospitals. PATIENTS: Infants at <30 weeks + 6 days gestational age on caffeine between 32 weeks and 36+5 days PMA in room air with routine caffeine discontinuation prior to 36 weeks +6 days. INTERVENTION: Randomisation to caffeine or placebo and treated through 42 completed weeks. Pulse oximetry was recorded from enrolment through 1 week after stopping study drug. Blood for 12 inflammation-related biomarkers obtained at enrolment and 38 weeks' PMA and brain imaging after enrolment or <3 days of randomisation, and study end. MAIN OUTCOME MEASURE: Seconds/hour of oxygen saturation <90% from randomisation to study end. RESULTS: Randomised 160 subjects, 78 placebo, 82 caffeine. IH was less at every PMA with caffeine treatment from 34 (172.7 (123.4, 241.7); 84.7 (64.4, 111.4, p<0.01) through 41 weeks (73.0 (51.3, 103.7); 26.6 (18.5, 38.2, p<0.001). Adjusted TNF-α levels were 23% lower at follow-up in the caffeine group compared with placebo (p<0.02), without other biomarker differences. Paired brain imaging found no significant differences. CONCLUSIONS: Extended caffeine reduced the burden of IH in very preterm infants and may reduce inflammation. Further study is needed to determine if this effect of caffeine is associated with reduced risk of adverse outcomes. TRIAL REGISTRATION NUMBER: NCT03321734.
19 Apr 2026
Read appraisal →Journal of medical Internet research
Comparing Pulmonary Telerehabilitation and Center-Based Pulmonary Rehabilitation for Effectiveness and Adherence in Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Pulmonary rehabilitation (PR) is a cornerstone of chronic obstructive pulmonary disease (COPD) management; however, access to traditional center-based PR (CBPR) remains limited. Digital and remote models, collectively termed pulmonary telerehabilitation (Tele-PR), have increasingly been used, but their heterogeneity in technology use, supervision, and interaction mode may influence effectiveness and sustainability. OBJECTIVE: This systematic review and meta-analysis aimed to compare the effectiveness and adherence of Tele-PR with those of CBPR in adults with COPD while systematically evaluating the impacts of supervision intensity and delivery models on key clinical outcomes. METHODS: This review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension) guidelines. PubMed, Embase, the Cochrane Library, and the Web of Science were searched from inception to December 10, 2025, to identify randomized controlled trials comparing Tele-PR or home-based PR (HBPR) with CBPR in adults with COPD. Random effects meta-analyses were conducted using the Hartung-Knapp-Sidik-Jonkman method. Between-study heterogeneity was assessed using τ², I², and 95% prediction intervals. Risk of bias was evaluated with the Cochrane Risk of Bias 2 tool, and certainty of evidence was graded using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. RESULTS: Seventeen randomized controlled trials involving 1658 participants were included. After intervention, Tele-PR and CBPR showed comparable average effects on exercise capacity by 6-minute walk distance (k=9; n=950, 57.3%; mean difference -5.37 m, 95% CI -15.68 to 4.95; P=.26; τ²=103.97; I²=28.2%; 95% prediction intervals=-32.73 to 22.27). Although pooled effects were not statistically significant, substantial heterogeneity was observed across remote delivery models. Subgroup analyses linked digitally supported, synchronously supervised Tele-PR to less between-study variance across several outcomes, indicating greater consistency in treatment effects across different settings while revealing that low-technology HBPR yielded more variable outcomes, particularly in symptom burden. At long-term follow-up (≥6 mo), between-group differences in functional and symptom outcomes diminished, and short-term gains in exercise capacity did not consistently translate into increased daily physical activity. Certainty of evidence ranged from moderate to very low, mainly downgraded for performance bias, inconsistency across intervention models, and imprecision. CONCLUSIONS: Tele-PR may achieve short-term clinical outcomes comparable to CBPR. Distinct from prior reviews, we stratified remote programs by delivery models and supervision, identifying digitally supported Tele-PR and low-technology HBPR as 2 clinically distinct paradigms with differing consistency of effects. We further propose a structured "supervision gradient" to interpret model-dependent variability in effects across Tele-PR approaches, providing a context-sensitive framework for evidence-informed, model-specific implementation. Future remote rehabilitation should integrate real-time professional supervision and long-term behavioral maintenance to sustain benefits. Tele-PR may be particularly valuable for expanding PR access, while CBPR remains essential for patients requiring close in-person supervision or complex multidisciplinary care.
18 Apr 2026
Read appraisal →Thorax
Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning.
RATIONALE: Despite significant advances in patient care and outcomes, criteria for cardiopulmonary exercise testing (CPET) in risk stratification guidelines for lung resection have not been updated in over a decade. We hypothesised that CPET no longer holds additional predictive value for postoperative complications. METHODS: In this secondary analysis, we included lung resection candidates from two prospective, multicentre studies eligible for CPET and assessed with preoperative pulmonary function tests (PFTs) and arterial blood gas analysis. Postoperative pulmonary (PPCs) and cardiovascular complications (PCCs) were documented during hospitalisation. We trained five types of machine learning models applying nested cross-validation to predict complications and compared predictive performance based on four metrics, including area under the receiver operating characteristic curve (AUC-ROC). RESULTS: A total of 497 patients were included. PPCs developed in 71 (14%) patients. Adding CPET parameters to PFTs and baseline clinical data did not improve the ability of models to predict PPCs in unselected patients (AUC-ROC=0.72-0.78; p=0.47), nor in those meeting American College of Chest Physicians (ACCPs) (n=236; AUC-ROC=0.64-0.78; p=0.70) or European Respiratory Society/European Society of Thoracic Surgery (ERS/ESTS) criteria (n=168; AUC-ROC=0.59-0.76; p=0.92). PCCs developed in 90 (18%) patients. CPET parameters likewise did not improve model performance for the prediction of PCCs in unselected patients (AUC-ROC=0.65-0.73; p=0.96), nor in the ACCP (AUC-ROC=0.61-0.73; p=0.82) or ERS/ESTS subgroups (AUC-ROC=0.62-0.69; p=0.87). CONCLUSIONS: In contemporary surgical practice, CPET did not improve the predictive performance of machine learning models for PPCs or PCCs in patients with an indication based on established guidelines or in those without. The role of CPET in preoperative risk stratification for lung resection should be re-evaluated. TRIAL REGISTRATION NUMBER: NCT03498352, NCT04826575.
17 Apr 2026
Read appraisal →Thorax
Digital twin-based bronchoscopy simulator improves training performance and skill retention of novices: a randomised controlled study
RATIONALE: Conventional bronchoscopy training often does not ensure lasting skill retention or adaptability to different anatomies, limiting real-world impact. This study used a digital-twin bronchoscopy simulator with various CT-derived bronchial tree models to better train novices. OBJECTIVES: To explore training with various anatomically diverse bronchial tree models in novices' bronchoscopy performance. METHODS: 60 bronchoscopy-naive participants were randomly assigned to three groups (n=20 each): control (written instruction only), anatomic-uniformity (trained on one standard bronchial model) and anatomic-variety (trained on multiple patient-derived bronchial models). All participants performed two tests: test 1 on a standard model and test 2 on a new CT-derived model. Both tests were repeated 3 months later to assess skill retention. The primary comparison was between the anatomic-variety and anatomic-uniformity groups. MEASUREMENTS AND MAIN RESULTS: 60 participants completed tests 1 and 2. 55 returned at 3 months. In test 1, there were no significant differences between the anatomic-variety and anatomic-uniformity groups in diagnostic completeness (DC, 0 segments, p=0.576), structured progress (SP, 1 correct progression, p=0.091) and procedure time (31 s, p=0.831). In test 2, the anatomic-variety group had significantly higher DC (2.5 segments, p<0.001) and SP (9 progression, p<0.001) than the anatomic-uniformity group. At 3 months, the anatomic-variety group retained superior DC and SP scores in both tests despite slight declines. CONCLUSIONS: Training with diverse anatomical models significantly enhanced bronchoscopy performance compared with repetitive practice on a single standardised model with partially maintained learning gains at 3 months.
17 Apr 2026
Read appraisal →Thorax
Multidisciplinary, non-pharmacological breathlessness intervention service for patients with moderately severe to severe COPD: a randomised controlled trial
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an often-progressive respiratory disease associated with disabling breathlessness. Breathlessness intervention services (BIS), which coach patients to self-manage breathlessness using non-pharmacological strategies, are effective in a variety of populations, including those with cancer and serious respiratory disease. This study aimed to compare the impact of the Westmead Breathlessness Service in people with moderate to severe COPD. METHODS: We analysed 113 participants randomised (1:1) with moderate/severe COPD (forced expiratory volume in 1 s (FEV1)/forced vital capacity <0.70 and FEV1 ≤60% predicted) and disabling breathlessness (modified Medical Research Council (mMRC) Breathlessness Score ≥2) to either an 8-week intervention involving breathing techniques, handheld fan use, exercise, energy conservation, dietetic advice (n=54) or 8-week wait-list control group (n=59). The primary outcome was change in Chronic Respiratory Questionnaire (CRQ) Mastery of breathlessness subscale. Secondary outcomes included change in other CRQ subscales (Fatigue, Emotion and Dyspnoea), exertional breathlessness intensity/unpleasantness (0-10 Numerical Rating Scale Score), anxiety and depression. Difference in change over 8 weeks between groups was compared using ANCOVA; p<0.05 statistically significant. FINDINGS: Participants were aged 70.9 (±8.5) years, 50% female, mean FEV1 =0.8 L (±0.3 L; 34% predicted), mMRC Breathlessness Score 3 (IQR 3-4). CRQ-Mastery improved following intervention compared with control (between-group difference 0.5 units; 95% CI 0.2 to 0.8; p=0.0262) using modified intention-to-treat analysis. Better CRQ-Dyspnoea and CRQ-Fatigue were seen in the intervention group (between-group difference-CRQ-Dyspnoea 0.4 units; CI 0.1 to 0.7; p=0.005, and CRQ-Fatigue 0.4 units; CI 0.1 to 0.7; p=0.014). Exertional breathlessness intensity (difference -0.8 units; CI -1.4 to -0.2; p=0.013) and breathlessness unpleasantness (difference -1.2 units; CI -1.7 to -0.6; p=0.001) also improved. INTERPRETATION: An 8-week BIS improved CRQ-Mastery, Dyspnoea and Fatigue, exertional breathlessness intensity and unpleasantness in people with severe COPD.
17 Apr 2026
Read appraisal →PloS one
LungNet: Leveraging state-space models with SE-enhanced skip connections for precise CT-based lung lesion segmentation
Lung cancer, which accounted for 2.48 million new cases and 1.82 million deaths worldwide in 2022, continues to be the most lethal cancer across the globe, underscoring the urgent demand for more advanced diagnostic tools. Although computed tomography (CT) imaging has long been central to lung cancer detection, the heterogeneous and complex characteristics of lung lesions make accurate segmentation particularly challenging. Current deep learning methods face a critical bottleneck: convolutional neural networks (CNNs) often struggle to capture long-range dependencies due to limited receptive fields, while Transformer-based architectures incur prohibitive computational costs when processing high-resolution CT volumes. Furthermore, standard skip connections in traditional UNet models frequently introduce redundant noise, leading to the dilution of subtle lesion features. To address these specific technical gaps, we introduce a novel deep learning framework that integrates Mamba state-space models with an improved UNet architecture. In this design, to mitigate feature redundancy, Squeeze-and-Excitation networks are embedded into skip connections, while auxiliary losses are introduced to address the degradation of shallow features and capture fine-grained lesion features across varied morphologies. Such a framework not only accommodates the intricate differences in lesion size, shape, and spatial distribution but also achieves a balance between global context modeling and linear computational efficiency. By uniting the local feature extraction strengths of convolutional layers with the long-range dependency modeling power of state-space models, our approach achieves more precise delineation of lung lesion boundaries. Extensive experiments conducted on multiple datasets provide compelling evidence of the method's effectiveness: it attains state-of-the-art segmentation accuracy and demonstrates significant promise for enhancing early detection, ongoing disease monitoring, and treatment planning in lung cancer patients. This advancement delivers a robust solution for the inherently complex task of lung lesion segmentation. Moreover, because lung cancer treatment is costly and insurance coverage plays a decisive role in distributing expenses, the study's outcomes also carry considerable implications for the insurance sector.
17 Apr 2026
Read appraisal →Sleep
Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography
STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest temperature, and finger photoplethysmography and finger temperature. METHODS: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography at a tertiary care sleep lab. Each polysomnography recording was manually scored, and these annotations served as ground truth labels for training and evaluation of our models. Polysomnography and wearable sensor data were automatically aligned using their electrocardiography channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. RESULTS: After ensembling, the model attains a 3-class (wake, non-rapid eye movement sleep, rapid eye movement sleep) balanced accuracy of 84.02 per cent, F1 score of 84.23 per cent, Cohen's κ of 72.89 per cent, and a Matthews correlation coefficient (MCC) score of 73.00 per cent; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30 per cent, F1 score of 74.10 per cent, Cohen's κ of 61.51 per cent, and MCC score of 61.95 per cent; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11 per cent, F1 score of 66.15 per cent, Cohen's κ of 53.23 per cent, MCC score of 54.38 per cent. CONCLUSIONS: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography, and can be applied to data from adults attending a tertiary care sleep clinic.
17 Apr 2026
Read appraisal →Sleep
Augmenting management of obstructive sleep apnea with consumer wearable devices to increase use of positive airway pressure therapy: a pilot randomized trial
STUDY OBJECTIVES: This pilot randomized trial measured the effects of a consumer-grade wearable-augmented program, which provides nightly pulse oximetry data to patients in a weekly report, on positive airway pressure (PAP) use in adults with obstructive sleep apnea (OSA) recruited from an outpatient sleep center. METHODS: This single-blind, parallel, two-group waitlist-controlled, single-site randomized trial recruited individuals with OSA with insufficient PAP use from a Department of Veterans Affairs medical center and randomized them to immediate participation in the consumer wearable-augmented self-monitoring program versus waitlist. The primary outcome was the change in modem-measured PAP use (mean hours or minutes per night over a 7-day period) from baseline (T1) to T3 assessment, which was 35 days following randomization (7 days post-intervention for the immediate group and prior to intervention for the waitlist group). RESULTS: We randomized 50 participants (mean age 47.9 years [SD 11.4]; 88% male). The change in PAP use was +1.33 hours (+79.8 minutes) for the immediate group and -0.17 hours (-10.2 minutes) for the waitlist control group, with a between-group difference of 1.50 hours (90 minutes) (p = .0097). The change in the number of days PAP was used for 4 hours or more over a 7-day period was +1.32 days for the immediate and 0.12 days for the waitlist control group, with a between-group difference of 1.20 (p = .0401). CONCLUSIONS: This pilot trial demonstrated that the consumer-grade wearable-augmented program is a promising intervention for increasing PAP use. A future full-scale trial that follows participants for a longer period is needed. Statement of Significance This pilot trial tested a novel use of the pulse oximetry feature available on some consumer-grade wearable devices to increase use of positive airway pressure (PAP) devices among adults with obstructive sleep apnea (OSA) and insufficient PAP use. The widespread and growing use of consumer-grade wearable devices offers an opportunity to leverage the features on these devices to tackle a common, problematic issue among individuals with OSA-insufficient use of PAP therapy. The randomized controlled design provides support for future research involving these devices.
17 Apr 2026
Read appraisal →BMJ open respiratory research
Personalised management of community-acquired pneumonia and the role of Clinical Decision Support Software
Community-acquired pneumonia (CAP) is one of the most common causes of hospital admission and is associated with significant morbidity and mortality. National and international guidelines are available to guide the management of CAP, including antibiotic prescribing. However, these guidelines are often not adhered to and there is significant overprescribing of broad-spectrum antibiotics, contributing to the growing pandemic of antimicrobial resistance. Clinical Decision Support Software (CDSS) are electronic tools that use individual patient data to generate patient specific assessments or recommendations, that can then be acted on by the patient or clinical decision maker. This article reviews the evidence surrounding the initial management of CAP in hospital and considers the potential of CDSS to support CAP management.
17 Apr 2026
Read appraisal →JMIR medical informatics
Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study
BACKGROUND: Nowadays, lung cancer has a significantly high incidence rate worldwide. The mortality rate of lung cancer continues to rise; it is more common in middle-aged and older individuals and poses a great threat to human health. OBJECTIVE: This study aimed to assess the lung cancer risk among middle-aged and older individuals in a timely manner and to establish an efficient pathway for the risk transfer. METHODS: We proposed a deep reinforcement learning model based on deep Q-network (DQN) to explore the risk transfer pathway for lung cancer among middle-aged and older individuals. Risk stratification of lung cancer occurrence was deduced through deep neural network. The DQN model was developed using the Health and Retirement Study cohort for model training and internal validation. We also used the China Health and Retirement Longitudinal Study cohort for model external validation. Transfer simulation of multiple pathways in different cycles was calculated in a stratified risk groups-leveraged DQN model. RESULTS: We developed and evaluated the DQN method for optimizing the risk transfer pathway among middle-aged and older individuals, with accuracy ranging from 0.917 (95% CI 0.896-0.928) to 0.949 (95% CI 0.909-0.961) and area under curve ranging from 0.906 (95% CI 0.887-0.933) to 0.927 (95% CI 0.893-0.938). External validation was conducted to assess the model's effectiveness and availability. A total of 8780 and 3763 samples from the Health and Retirement Study were used for model training and internal testing, respectively, and 16,442 samples from the China Health and Retirement Longitudinal Study were used for external validation. The results showed that DQN models illuminated the optimal risk transfer pathways for stratified risk groups. Lung cancer incidence in the high risk group had declined by 68.2% through risk transfer of model-based simulation, which had declined by 56.9% in the medium risk group. Through simulative intervention and risk transition deduced from the DQN model, lung cancer incidences of the high risk, medium risk, and low risk groups were obviously decreased. CONCLUSIONS: A DQN-based deep reinforcement learning model was proposed and validated to develop and simulate a risk transfer pathway of lung cancer among middle-aged and older individuals. Risk stratification supplied an effective foundation for lung cancer risk transition.
17 Apr 2026
Read appraisal →Stem cell reports
Cellular immunotherapy for COVID-19-induced acute respiratory distress syndrome: Results of the CIRCA-19 phase 1 safety and phase 2 randomized controlled trials
The ability of immunomodulatory mesenchymal stromal cells (MSCs) to improve COVID-19-associated acute respiratory distress syndrome (ARDS) in clinical trials is uncertain. We assessed whether freshly cultured umbilical cord (UC)-derived MSCs improved outcomes in patients with severe COVID-19 ARDS. We enrolled 37 patients with severe COVID-19 ARDS: 15 in the phase 1 dose escalation and open label extension studies (NCT04400032), and 22 patients in the phase 2b randomized clinical trial (NCT04865107). Delivery of up to 270 × 106 MSCs in three divided daily doses was well tolerated and resulted in qualitative improvement in all clinical outcomes. Furthermore, MSCs resulted in resolution of lymphopenia, consistent with an important immunomodulatory effect, with significant improvement in patient reported quality-of-life measures (SF-36) at 6 months pointing to possible durable clinical effects. These findings suggest a potential benefit of freshly cultured, UC-MSCs in severe COVID-19 ARDS, associated with biological evidence of favorable immunomodulatory activity.
17 Apr 2026
Read appraisal →BMJ open
Association between ambient air pollution exposure levels and the risk of developing pulmonary embolism in the Chinese population: findings from the Chinese Cardiovascular Association (CCA) Database-Chest Pain Centre
OBJECTIVE: To analyse the associations between exposure to different air pollutants and the morbidity of pulmonary embolism (PE). DESIGN: Time series study with a distribution lag non-linear model. SETTING: 275 cities in China, 1 January 2015 to 31 March 2022. PARTICIPANTS: 27 369 cases of PE. MAIN OUTCOME MEASURES: The associations between exposure to different air pollutants and the morbidity of PE. RESULTS: During the study period, 27 369 cases of PE were recorded. PM2.5(Particulate Matter 2.5), PM10(Particulate Matter 10) and NO2(Nitrogen Dioxide) exposure levels were associated with an increased risk of developing PE in the single-pollutant model. PM2.5 exposure caused the greatest risk of developing PE at lag 1 in the single-day lag model, with a relative risk (RR) value of 1.033 (95% CI 1.007 to 1.058). PM10 and NO2 exposure had the greatest effects at lag 0, with RR values of 1.038 (95% CI 1.016 to 1.059) and 1.039 (95% CI 1.009 to 1.068), respectively. No associations were found between CO(Carbon Monoxide), O3(Ozone) or SO2(Sulfur Dioxide) exposure levels and the risk of developing PE. PM2.5, PM10 and NO2 exposure levels were most strongly associated with the development of PE at lag 01 in the cumulative lag model, with RR values of 1.052 (95% CI 1.009 to 1.095), 1.053 (95% CI 1.020 to 1.086) and 1.058 (95% CI 1.005 to 1.111), respectively. CONCLUSIONS: Short-term exposure to PM2.5, PM10 and NO2 can increase the risk of developing PE, especially in females. The effects of PM2.5 and PM10 exposure were more significant in cold seasons and in North China.
17 Apr 2026
Read appraisal →A&A practice
Automated Identification of Cardiopulmonary Disease Cases for Preoperative Risk Stratification Using Machine Learning: A Retrospective Analysis
BACKGROUND: Preoperative chart review is time-consuming and prone to errors, particularly for cardiopulmonary conditions that impact anesthetic planning. We developed a guideline-aligned "clinical insight bot" that mines free-text documentation to surface perioperative cardiovascular risk signals relevant to the 2024 Mult Society perioperative guideline for noncardiac surgery. METHODS: We analyzed 1000 de-identified medical cases from the PhysioNet MIMIC database. Medical terminology was extracted using regex-based NLP and categorized into 13 clinical specialties. Text features were encoded using TF-IDF vectorization and 1536-dimensional semantic embeddings stored in a PostgreSQL vector database (pgvector). Four machine learning models-Logistic Regression, Random Forest, Support Vector Machine (SVM), and Naive Bayes-were trained with stratified fivefold cross-validation to classify cases as "cardiopulmonary-only" versus "mixed/other." Performance was evaluated using accuracy, precision, recall, and F1 score, with statistical comparison via McNemar's test and bootstrap confidence intervals. RESULTS: In a held-out test set of 200 notes (28 positive; 172 negatives; ~14% prevalence), a linear support vector machine achieved the best overall balance (F1 ≈ 0.71), with high precision (positive predictive value 0.94) and very low false positive rate (FPR) (1/172 ≈ 0.6%). False negatives were the dominant residual error class. The pipeline processed documents near-instantaneously and, when scaled to 1000 notes, replaced on the order of tens of clinician review hours (≈100× efficiency gain) while maintaining performance across common preoperative document types. CONCLUSIONS: A lightweight, guideline-aligned insight bot can transform unstructured preoperative notes into concise, stepwise prompts that flag cardiovascular risk signals before the day of surgery. High precision with a very low FPR supports safe integration with anesthesiology workflows by minimizing paging noise, whereas time savings create operational and financial value. Future work should emphasize multicenter validation, structured data fusion (including labs, imaging, and vitals) to improve sensitivity, and prospective evaluation of downstream clinical and operational outcomes.
17 Apr 2026
Read appraisal →Emerging microbes & infections
Cross-protection against highly pathogenic avian influenza H5N1 virus from seasonal influenza vaccines: a systematic review and meta-analysis of ferret studies
The recent surge in spillover events of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b to humans and mammals in North America has raised urgent pandemic concerns. Human H5N1 vaccines are unavailable in most countries. We synthesized data from ferret challenge trials to evaluate whether widely available seasonal influenza vaccines confer cross-protection against lethal H5N1 infection. We systematically searched PubMed, Embase, and Web of Science for ferret studies of lethal H5N1 challenge published up to 5 July 2025 (PROSPERO #CRD42024520346). Random-effects meta-analyses were conducted to compare vaccine efficacy (VE) of seasonal influenza vaccines and H5N1 vaccines against H5N1-related mortality. Seroprotection was defined as a neutralizing antibody titre of ≥1:40. We identified 35 studies (157 trials). Seasonal influenza vaccines without N1 did not confer significant cross-protection (five trials; VE 14.8%, 95% CI -3.6 to 30.0). In contrast, VE was 73% for N1-containing seasonal influenza vaccines (19 trials; 95% CI 54-84) and 77% for H5N1 vaccines overall (133 trials; 95% CI 72-82) (p = 0.52). The VE of N1-containing seasonal influenza vaccines was modestly lower than that of H5N1 vaccines with seroprotection (88%; 66 trials; 95% CI 84-91; p = 0.009), but comparable to H5N1 vaccines that did not achieve seroprotection (63%; 67 trials; 95% CI 52-71; p = 0.29). The VE of seasonal influenza vaccines against H5N1 was robust across sensitivity analyses, with no evidence of publication bias (p = 0.99). Seasonal influenza vaccines significantly reduce H5N1-associated mortality in ferret trials, suggesting the cross-protection potential of currently available vaccines. Human studies are warranted.
16 Apr 2026
Read appraisal →Neuroscience
Neural responses to acute hypoxia and hyperoxia
The brain's high metabolic demands make it highly sensitive to oxygen level changes, with both hypoxia and hyperoxia affecting cognitive and neural function. However, the underlying neural mechanisms remain unclear. This study aimed to characterise the effects of acute normobaric hypoxia and hyperoxia on neural function and perceptual thresholds using EEG, visual evoked potentials (VEPs), and critical flicker fusion (CFF) thresholds. In a single-blind, randomised, crossover design, thirty participants inhaled 10.5%, 21%, or 100% oxygen while resting-state EEG, VEPs, and CFF thresholds were recorded. Hypoxia reduced CFF thresholds in an exposure-dependent manner, while hyperoxia produced a smaller, transient reduction. Resting-state EEG revealed significantly increased power across delta, theta, and beta bands in response to hypoxia. Whereas hyperoxia induced smaller increases in theta and alpha and decreases in gamma power and a reduction in beta power over the sensorimotor area. Hypoxia increased spectral entropy, indicating impaired network synchronisation, while hyperoxia increased fuzzy entropy, particularly in eyes-closed states, potentially reflecting enhanced neural complexity. In VEPs, hypoxia reduced P1 amplitude and increased N2 deflections, reflecting disrupted early and late sensory processing, with sustained effects across blocks. Hyperoxia transiently increased P1 amplitude and showed minor effects on other components. These findings demonstrate the impact of altered cerebral oxygen on fundamental neural processing and the associated state- and time-dependent effects on brain function. This provides insight into the mechanisms underlying oxygen-induced neuromodulation and highlights the value of EEG and CFF as biomarkers for tracking neural responses to oxygen variability.
16 Apr 2026
Read appraisal →The New England journal of medicine
Selective Decontamination of the Digestive Tract during Ventilation in the ICU
BACKGROUND: Whether selective decontamination of the digestive tract (SDD) reduces mortality among patients undergoing mechanical ventilation and whether it adversely affects microbial ecology in the intensive care unit (ICU) remain unclear. In an earlier analysis of data from Australia, SDD did not result in a lower incidence of in-hospital death than standard care, but data from the full international trial are needed. METHODS: We randomly assigned ICUs in Australia and Canada to use SDD or to continue standard care for two 12-month periods in patients undergoing mechanical ventilation. Patients in the SDD group received specific oral and gastric antimicrobial interventions for the duration of ventilation and an intravenous antibiotic agent for the first 4 days after enrollment. All other patients in the ICU were included in an observational ecologic assessment. Previously reported data from Australia are now combined with data from Canada. The primary outcome was in-hospital death from any cause at 90 days. The secondary clinical outcomes, assessed at 90 days, were death in the ICU and the number of days alive and free of mechanical ventilation, ICU admission, and hospitalization. Microbiologic secondary outcomes included new positive cultures for bloodstream infections and antibiotic-resistant organisms. For the ecologic assessment, the microbiologic outcomes were tested for noninferiority (noninferiority margin, 2 percentage points). RESULTS: In this trial involving 20,000 patients in 26 ICUs, 9289 patients were enrolled in the randomized trial and 10,711 were included in the ecologic assessment. At 90 days, 1175 of 4215 patients (27.9%) in the SDD group and 1494 of 5065 (29.5%) in the standard-care group had died before hospital discharge (odds ratio, 0.94; 95% confidence interval [CI], 0.84 to 1.05; P = 0.27). New bloodstream infections occurred in 4.9% of the patients in the SDD group and in 6.8% of those in the standard-care group (adjusted mean difference, -1.30 percentage points; 95% CI, -2.55 to -0.05); antibiotic-resistant organisms were cultured in 16.8% and 26.8%, respectively (adjusted mean difference, -9.60 percentage points; 95% CI, -12.40 to -6.80). In the ecologic assessment, noninferiority of SDD was not confirmed for the development of new antibiotic-resistant organisms. Adverse events considered to be related to SDD or standard care were reported in 12 patients (0.3%) in the SDD group and in no patients in the standard-care group. Serious adverse events occurred in 47 patients (1.1%) and 59 patients (1.2%), respectively. CONCLUSIONS: Among critically ill patients undergoing mechanical ventilation, SDD did not result in a lower incidence of in-hospital death than standard care. (Funded by the National Health and Medical Research Council of Australia and the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT02389036.).
16 Apr 2026
Read appraisal →Global health action
Impact of climate change on pediatric health outcomes
Climate change has become one of the most critical health issues globally in the twenty-first century with children bearing the disproportionate burden of the burden since they are more vulnerable than adults because of their physiological, behavioral, and developmental capacities. It is a systematic review that rates the evidence of the relationship between climatic exposures such as heat, air-pollution, and extreme weather events and pediatric health outcomes. The number of peer-reviewed studies involved was 23 published in 2000-2025, which represented different geographic areas and study designs and assessed acute and chronic health outcomes. The Newcastle-Ottawa Scale and the ROBINS-I tool were used to evaluate the methodological quality, and the majority of the studies had low to moderate risks of bias. The narrative synthesis shows that there are always links between air pollutants especially PM2.5, NO2 and O3 and respiratory morbidity, prevalence of asthma and hospitalization of children. Amplified temperatures as well as heat waves were associated with increased cases of heat illness, dehydration, and febrile state in infants and young children. There were elevated cases of diarrheal and vector-related infections, especially in low-resource settings, which were linked to extreme weather events especially floods. Although the overall results were similar, significant differences in the regions and methods were found, and low-income countries show little evidence. In addition, exposures as analyzed in most studies were usually considered individually, which may have underestimated the cumulative or compound climate risks. Main findings: This review shows that rising temperatures, air pollutants, and extreme weather events are consistently linked with higher rates of respiratory illness, heat-related illness, and infectious diseases among children.Added knowledge: The synthesis brings together two decades of evidence showing that children across multiple continents experience similar health vulnerabilities when exposed to climate-related environmental conditions.Global health impact for policy and action: The findings highlight an urgent need for stronger climate-responsive child health policies and early warning systems to protect children from worsening environmental threats worldwide.
16 Apr 2026
Read appraisal →Physiological measurement
Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Objective.This work aims to enable adaptive consumer sleep technologies (CSTs) for sleep intervention by developing a deep learning model for sleep stage classification using wearable sensor data.Approach.We propose an end-to-end deep learning approach leveraging Photoplethysmography (PPG) signals, commonly available in CSTs. Model performance is improved by pretraining with electrocardiography (ECG) from the large-scale multi-ethnic study of atherosclerosis dataset datasets. Training and evaluation are conducted with the Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology and an additional dataset comprising of 13 participants (aged 22-71 years) without prior known sleep disorders. The dataset contains combined synchronized polysomnography (PSG) and Empatica E4 wearable data, annotated with American Academy of Sleep Medicine (AASM) sleep stages.Main results.The proposed method demonstrates sleep stage classification from minimally processed PPG signals for real-time intervention. While ECG-trained models are not directly transferable to PPG, fine-tuning significantly improves performance, achieving up to a 29% increase in multi-stage classification accuracy.Conclusion.Pretraining with ECG and fine-tuning with PPG increases sleep stage classification for end-to-end deep learning models, exceeding previous efforts particularly in 3-stage sleep classification.Significance.This work contributes to sleep health by developing a sleep stage classification model for minimally processed PPG sensor data and takes a step further towards making adaptive CSTs feasible for use with wearable sensors.
16 Apr 2026
Read appraisal →Journal of investigational allergology & clinical immunology
Future Perspectives for Artificial Intelligence in Allergy: Advances in Diagnosis, Monitoring, and Personalized Care
Artificial intelligence (AI) is significantly transforming the specialty of allergy by offering novel tools for diagnosis, risk prediction, disease monitoring, and personalized care. This narrative review comprehensively explores key advances in the application of AI techniques-expert systems, machine learning, deep learning, and natural language processing-across various allergic diseases such as asthma, anaphylaxis, atopic dermatitis, food allergy, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions. Despite these advancements, several challenges persist, including model interpretability, external validation, and ethical and regulatory barriers. Nonetheless, AI holds strong potential to enhance diagnostic efficiency, therapeutic precision, and patient outcomes. This review presents an updated perspective on the state of the art of AI in allergy, highlighting both its achievements and ongoing challenges.
16 Apr 2026
Read appraisal →Cancer cell
HS-20093, a B7-H3-targeted antibody-drug conjugate in lung cancer: Results from the ARTEMIS-001 phase 1a/b trial
This phase 1a/b study (NCT05276609) evaluated the safety, pharmacokinetics, and efficacy of B7-H3-targeted antibody-drug conjugate HS-20093 (GSK5764227) in 306 patients with previously treated advanced solid tumors. In phase 1a, 12.0 mg/kg was established as the maximum tolerated dose. Among 236 lung cancer patients who received 8.0 or 10.0 mg/kg HS-20093, the most frequent grade ≥3 treatment-related adverse events (AEs) included decreased neutrophil (25.5% vs. 50.5%) and white blood cell counts (19.7% vs. 42.4%), and anemia (16.8% vs. 34.3%), respectively. Treatment-related interstitial lung disease and AEs leading to death occurred in 3.4% and 3.8% of patients, respectively. Among response-evaluable patients, the confirmed objective response rate was 52.3% (95% CI: 39.5, 64.9) for extensive-stage SCLC (ES-SCLC, N = 65) and 22.4% (95% CI: 16.0, 29.8) for non-small cell lung cancer (NSCLC, N = 152) patients, with comparable rates between 8.0 mg/kg and 10.0 mg/kg dose cohorts across both groups. These results support further development of HS-20093, with 8.0 mg/kg selected for phase 3 trials.
16 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Effect of a Comprehensive Mobile-Based Respiratory Training Program on Respiratory Function in Survivors of Acute Stroke: Randomized Controlled Trial
BACKGROUND: Respiratory dysfunction frequently occurs during the acute phase of stroke and is associated with reduced ventilatory capacity, respiratory muscle weakness, and increased pulmonary complications. However, delivering standardized respiratory training during hospitalization is often constrained by staffing and service continuity. OBJECTIVE: This study aimed to evaluate the efficacy, safety, and feasibility of a hospital-based comprehensive mobile-based respiratory training program (CMRTP) added to conventional rehabilitation in people with acute stroke who are inpatients. METHODS: This single-center, assessor-blinded randomized controlled trial enrolled 40 patients within 2 weeks after stroke onset with respiratory dysfunction (forced vital capacity <80% predicted). Participants were randomized (1:1) to CMRTP plus conventional rehabilitation or conventional rehabilitation alone. The CMRTP was delivered via the WeChat-based AIRHUB platform and performed 20 minutes twice daily, 5 days per week for 2 weeks, either independently or with caregiver assistance as needed. The primary outcome was change in forced vital capacity from baseline to week 2. Secondary outcomes included forced expiratory volume in 1 second (FEV₁), peak expiratory flow, maximal inspiratory pressure, maximal expiratory pressure, and modified Barthel index. All outcomes were assessed face-to-face by a blinded senior physician, and all analyses followed an intention-to-treat principle. RESULTS: Of 56 screened patients, 40 were randomized, and 39 completed the study. Adherence to the CMRTP reached 96%, and no serious adverse events occurred; mild, transient events (fatigue, dizziness, and hyperventilation) were recorded. Compared with the control group, the CMRTP group demonstrated greater improvement in forced vital capacity at week 2 (mean difference 0.77 L; 95% CI 0.39-1.16; P<.001; η²=0.32), with additional between-group differences in maximal inspiratory pressure (P=.001; η²=.25), maximal expiratory pressure (P<.001; η²=.08), and modified Barthel index (P=.001; η²=.26). No significant group differences were found for forced expiratory volume in 1 second or peak expiratory flow. CONCLUSIONS: A 2-week hospital-based mobile respiratory training program is feasible and safe in people with acute stroke who are inpatients and yields clinically meaningful improvements in respiratory function and daily functional performance when added to conventional rehabilitation.
15 Apr 2026
Read appraisal →ChemMedChem
Structure- and Ligand-Based Discovery of Novel 3-Chymotrypsin-Like Protease Nonpeptidomimetic Hits
The SARS-CoV-2 3-chymotrypsin-like (3CLpro) protease is a key target for the development of COVID-19 therapeutics. While ensitrelvir and nirmatrelvir are approved drugs for treatment, the continuous research and development for new antiviral drugs is necessary to combat the emergence of variants and other related viruses. This study employed structure- and ligand-based computer-assisted approaches to identify new 3CLpro nonpeptidomimetic inhibitors. Using data from COVID Moonshot, NCATS, and the literature, computational methods such as shape-based, ensemble docking, and machine learning (ML) techniques were developed, achieving robust validation metrics: AUC = 87%, EF = 7, BEDROC = 60% for shape-based; AUC = 87%, EF = 7.03, BEDROC = 62% for ensemble docking, and ACC = 81%, MCC = 62% for ML models, combing Random forest + ECFP4 fingerprint. These models were utilized in virtual screening (VS) campaigns using the H3D and ChemBridge libraries, from which six promising hits with IC50 values ≤80 µM were identified, including LabMol-499 with an IC50 of 13.71 µM and a Ki of 21.74 µM. Moreover, we found that LabMol-499 acts as a noncompetitive, reversible inhibitor of 3CLpro. These findings provide a foundation for hit-to-lead optimization of new nonpeptidomimetic 3CLpro inhibitors.
15 Apr 2026
Read appraisal →JAMA
Restrictive vs Liberal Physical Restraint Strategies in Critically Ill Patients: The R2D2-ICU Randomized Clinical Trial
IMPORTANCE: The effect of wrist-strap physical restraints on outcomes in patients receiving mechanical ventilation in the intensive care unit (ICU) remains uncertain. OBJECTIVE: To investigate the effect of a low-use wrist-strap physical restraint strategy in critically ill patients receiving invasive mechanical ventilation. DESIGN, SETTING, AND PARTICIPANTS: Open-label randomized clinical trial conducted across 10 ICUs in France. Between January 5, 2021, and January 2, 2024, 405 adult patients who had initiated invasive mechanical ventilation within the previous 6 hours and were expected to require ventilation for at least 48 hours were enrolled. Follow-up was completed on May 17, 2024. Statistical analysis was conducted from June 1, 2025, to December 15, 2025. INTERVENTIONS: Patients were randomized to undergo either a restrictive, low-use physical restraint strategy (wrist straps avoided unless necessary because of severe agitation, defined as a Richmond Agitation-Sedation Scale score of ≥3 [on a scale from -5 (unresponsive) to 4 (combative)]; n = 201) or a liberal, high-use strategy (wrist straps applied systematically and reassessed daily; n = 204). Discontinuation of restraints was allowed in patients who were awake or extubated without delirium (measured via the Confusion Assessment Method for the ICU). MAIN OUTCOMES AND MEASURES: The primary outcome was the number of days alive without coma or delirium during the first 14 days after randomization. Secondary outcomes included incidence of self-extubation and day-90 mortality. RESULTS: Among 396 patients with available primary outcome data, the median (IQR) age was 65 (56-73) years, 245 (62%) were male, and the median (IQR) Sequential Organ Failure Assessment score was 7 (4-10). The mean days alive without coma or delirium were 6.67 days (95% CI, 5.69-7.65) in the low-use strategy group and 6.30 days (95% CI, 5.35-7.24) in the high-use strategy group (adjusted mean difference, 0.37 days [95% CI, -0.71 to 1.46]; P = .51). Self-extubation occurred in 18 patients (9.2%) in the low-use strategy group and 17 (8.5%) in the high-use strategy group, and day-90 mortality was 37.2% and 41.0%, respectively. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, among adult patients receiving mechanical ventilation in the ICU, a low-use wrist-strap physical restraint strategy compared with a high-use strategy did not reduce days free of delirium or coma at 14 days. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04273360.
15 Apr 2026
Read appraisal →Current pain and headache reports
Stellate Ganglion Block in the Treatment of Long COVID: A Systematic Review
OBJECTIVES: This review evaluates stellate ganglion block as a treatment for long COVID, seeking to evaluate the treatment's efficacy by various symptoms and the limitations of the current literature. STUDY DESIGN: Systematic Review. SETTING: Ambulatory or Outpatient Setting. METHODS, SUBJECTS: A systematic review of the current literature regarding use of stellate ganglion block in patients with long COVID was conducted. 2 databases were searched on August 28th, 2025. Search terms were "long COVID" and "stellate ganglion block", yielding 45 results. Studies examining patient outcomes after stellate ganglion block were included. Case reports, case series, basic science studies and previous reviews were excluded. Seven studies met inclusion criteria. RESULTS: Patients received a single stellate ganglion block in some studies and multiple stellate ganglion blocks in others. All studies reported symptomatic improvement without control groups. Response rates ranged from 55.8% to 100%. The most robust improvements (> 80% patients reporting relief) were seen in cough, dyspnea, headache, joint pain, pain interference/intensity, pins/needles, subjective relief. CONCLUSION: Stellate ganglion block is a promising treatment that appears to generate substantive benefit for many of the symptoms seen in long COVID. However, the current literature has small, uncontrolled studies with heterogenous study designs and follow-up periods. Standardized research with larger sample sizes, control groups, and longer-term follow up is necessary to elucidate the degree of benefit. IRB approval and clinical trial registration not required.
15 Apr 2026
Read appraisal →Nature communications
A group 1 hemagglutinin stem vaccine elicits broad humoral responses against influenza in phase 1/2a study
Vaccines targeting the conserved stem region of influenza hemagglutinin (HA) may induce broad immunity and reduce reliance on strain-matched seasonal formulations. This Phase 1/2a study evaluates safety and immunogenicity of a universal influenza vaccine component comprising a soluble trimeric stabilized group 1 HA-stem protein (INFLUENZA G1 mHA). Healthy adults aged 18-to-45 received one or two doses at 45 µg or 135 µg, with or without Al(OH)₃. Primary endpoints were safety and reactogenicity. All regimens were well-tolerated with no vaccine-related serious adverse events. Secondary and exploratory endpoints included binding, neutralizing, and ADCC responses compared to a matched quadrivalent seasonal influenza vaccine cohort. A single dose induced robust binding antibody responses to H5 HA and homologous stem HA, with 6.5-16.4-fold increases at Day 29 versus baseline, exceeding responses elicited by the seasonal influenza vaccine. Additionally, we demonstrate broader binding to a panel of group 1 HAs, with responses maintained for at least one year. Neutralizing antibody and ADCC responses at Day 29 showed 7.9-15-fold and 4.9-8.6-fold increases, respectively. Importantly, serum from vaccinated participants protect mice against lethal heterologous H5N1 challenge. This study provides proof of concept that the HA stem vaccine elicits broad and durable influenza A group 1-directed immunity. This trial is registered with ClinicalTrials.gov (NCT05901636).
15 Apr 2026
Read appraisal →Tobacco control
Cigarette filter ventilation, product appeal and regulatory options: a review of the influence of ventilation on consumers' sensory and risk perceptions
OBJECTIVE: Cigarette filter ventilation may enhance product appeal, support initiation and sustained smoking, and increase smoking prevalence. We reviewed recent evidence on how filter ventilation affects consumers' perceptions of product appeal and considered product use preferences to inform regulatory options. DATA SOURCES: PubMed, Embase and PsycINFO databases were searched up to December 2023. Systematic search strategies used terms based on cigarette filter ventilation, and multiple constructs comprising product appeal and user perceptions. STUDY SELECTION: Two reviewers screened all 917 retrieved titles and abstracts independently and applied a consensus strategy to identify 16 articles that met eligibility criteria. DATA EXTRACTION: Extracted data focused on study design, dependent variables or outcomes, and major conclusions. DATA SYNTHESIS: While remaining popular, awareness and risk perception of ventilated cigarettes is generally low. Cigarettes with filter ventilation are perceived as smoother and less irritating than non-ventilated cigarettes. These modified sensory experiences shape perceptions of reduced health risks. Pack text descriptors and colours convey further information to consumers about smoothness, supporting lowered risk perceptions. CONCLUSIONS: Filter ventilation and its associated marketing continue to influence consumers' perceptions of smoothness creating misperceptions about health risks. Standardised ('plain') packaging regulations are recommended to restrict communication of ventilation themes as well as evidence-based health communications to correct consumer misperceptions about smoothness and health risks. Since filter ventilation has no benefits for public health, policy makers could consider banning filter ventilation or even disallowing filters.
15 Apr 2026
Read appraisal →Tobacco control
Nitrosamine and nicotine exposure after switching from filtered to unfiltered cigarette smoking: a cross-over clinical trial
BACKGROUND: The cellulose acetate filter is a plastic attachment on nearly all commercial cigarettes sold worldwide. It is the main component of discarded cigarette butts, and the most prevalent waste item collected during urban and beach cleanups. This waste leaches toxic chemicals, including nicotine, metals and tobacco-specific nitrosamines and contributes to environmental microplastic pollution. There is growing international interest in reducing plastic waste from single-use, non-essential products such as cellulose acetate cigarette filters. Public health and environmental advocates recommend a ban on the sale of filtered cigarettes to reduce environmental pollution caused by discarded cigarette butts and to discourage cigarette smoking. RESEARCH QUESTION: What potential health and behavioural implications might arise among people who smoke if filtered cigarettes are removed from the market. METHODS: We conducted a cross-over, randomised clinical trial of 29 people who smoke to evaluate changes in biomarkers for nicotine and tobacco-specific nitrosamine exposure when switching between filtered and unfiltered cigarette smoking. RESULTS: Although unfiltered smoking showed a higher trend in the geometric means of 4-[(methylnitrosamino])-1-[3-pyridyl]-1-butanol (NNAL) compared with filtered cigarettes, regression models for urinary cotinine and NNAL showed no significant differences when switching between filtered and unfiltered cigarettes. CONCLUSION: This proof-of-principle study suggests there is no increased risk to people who smoke when switching to unfiltered cigarette smoking. Although larger studies might provide more evidence regarding unfiltered cigarette smoking, banning the sale of filtered cigarettes may be an important policy intervention to both reduce hazardous tobacco waste and discourage smoking.
15 Apr 2026
Read appraisal →Cardiovascular toxicology
A Real-World Disproportionality Analysis of Drug-Induced Heart Failure: Integrating FAERS Database and AHA Statement
Heart failure (HF), the advanced stage of diverse cardiac conditions, poses a significant public health challenge. This study analyzes adverse event (AE) signals associated with HF within the FDA Adverse Event Reporting System (FAERS) database, offering critical insights to guide rational medication use in clinical settings. Adverse event reports (AERs) associated with HF in the FAERS database, spanning from the first quarter of 2004 to the fourth quarter of 2024, were analyzed. The potential HF risk of drugs was assessed using the disproportionality analysis, with findings juxtaposed against the American Heart Association (AHA) drug list. Drug-induced HF cases showed equivalent distribution between male and female, and high rates of rehospitalization and mortality. Prominent hypoglycemic drugs associated with HF included rosiglitazone, pioglitazone, and saxagliptin. Combination therapies, such as metformin/saxagliptin and metformin/rosiglitazone, also demonstrated elevated risks. In the context of pulmonary hypertension, drugs like macitentan, selexipag, and epoprostenol were particularly notable. Analysis against the AHA drug list confirmed stronger signal strength and frequency among Class A drugs, validating our results. Furthermore, drugs not flagged as positively signaling in the AHA registry, such as amiodarone, aliskiren, and testosterone, merit closer scrutiny due to their potential for adverse reactions. Our analysis of the FAERS database has identified medications associated with potential HF risks, providing valuable evidence to inform clinical decision-making and promote the safe use of medications.
13 Apr 2026
Read appraisal →Journal of prosthodontic research
Short- and long-term effects of mandibular advancement device therapy for obstructive sleep apnea on temporomandibular disorders: A systematic review and meta-analysis
PURPOSE: This systematic review and meta-analysis investigated the effects of mandibular advancement device (MAD) therapy on temporomandibular disorders (TMDs) in patients with obstructive sleep apnea (OSA). STUDY SELECTION: We included 14 clinical studies assessing the signs and symptoms of TMDs in patients with OSA treated with MAD therapy. The analysis was divided into short-term (<6 months) and long-term (≥1 year) follow-up phases. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for each study during follow-up. The certainty of the evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology, and meta-analysis was conducted using Review Manager version 5.4. RESULTS: Across the 14 studies included, the average follow-up period was 18.4 months, and the overall certainty of the evidence was rated as very low. Four studies used the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD), six used the Helkimo Anamnestic Index, and four employed questionnaires. In short-term follow-up, a significant increase in pain-related TMDs was observed, including pain in the temporomandibular joint (TMJ) or masticatory muscles (pain-related TMDs: OR:4.49; 95%CI: 1.46 to 13.81, TMJ or masticatory muscles pain: OR:2.90; 95%CI: 1.26 to 6.71). Conversely, during long-term follow-up, the odds of pain in the TMJ or masticatory muscles were significantly reduced (OR:0.21; 95%CI, 0.05 0.89)Conclusions: MAD therapy for OSA did not cause long-term adverse effects on the TMJ or masticatory muscles. However, its short-term use may result in pain-related TMDs, highlighting the need to monitor and manage these symptoms during this period.
13 Apr 2026
Read appraisal →Medicine
Effectiveness of Chinese herbal medicine mouthwash in preventing ventilator-associated pneumonia in patients with endotracheal intubation: A systematic review and meta-analysis
BACKGROUND: To systematically evaluate the effectiveness and safety of Chinese herbal medicine mouthwash (CHMM) in preventing ventilator-associated pneumonia (VAP) in patients with endotracheal intubation (ETT). METHODS: Computer searches were conducted for relevant randomized controlled trials (RCTs) in the China National Knowledge Infrastructure (CNKI), WANFANG DATA, Chinese Scientific and Technical Journal Database (VIP), Chinese Biomedical Literature Database (SinoMed), PubMed, Embase, Cochrane Library, and CINAHL Database from the establishment to April 5, 2023. The data were analyzed by RevMan 5.3 software. The methodological quality of papers was evaluated by the Cochrane version 5.1.0 and the certainty of evidence was evaluated by the GRADE system. RESULTS: We included 27 RCTs with 2426 patients. Most of the included studies were of poor methodological quality. Meta-analysis showed that CHMM (RR = 0.41, 95% CI: 0.34-0.50, P < .00001, low-certainty evidence) or CHMM + routine oral care (RR = 0.31, 95% CI: 0.18-0.56, P < .0001) could reduce the incidence of VAP compared to non-CHMM. CHMM may be superior to saline (low-certainty evidence) and chlorhexidine (moderate-certainty evidence) in preventing VAP (P < .05). CONCLUSION: Low-certainty evidence shows that CHMM may be more advantageous in reducing the incidence of VAP compared to non-CHMM. However, due to some limitations and the low certainty of evidence, we cannot recommend CHMM for routine clinical use at this time. Large-scale and high-quality RCTs are also needed to definitively evaluate the safety and clinical benefits of CHMM in the future.
12 Apr 2026
Read appraisal →BMJ open respiratory research
Occupational exposure to toxic particles and risk of pulmonary sarcoidosis: a systematic review and meta-analysis
INTRODUCTION: The onset of sarcoidosis is likely the result of a complex genetic-environment-immunological interaction. This systematic review and meta-analysis aimed to identify occupational toxic particles associated with an increased risk of developing pulmonary sarcoidosis. METHODS: Publications in English, published from January 2000 to January 2025, were searched in PubMed, Scopus and JSTOR. The risk of bias was assessed for all included studies. Sensitivity analyses stratified by study quality were conducted to evaluate the robustness of the findings and potential bias. To assess publication bias, a funnel plot was used when more than six studies were included in the analysis. RESULTS: Five categories of toxic particles were identified to be associated with increased risk of the onset of sarcoidosis: chemicals, inorganic dusts, metals, mixed dusts and fumes and organic dusts. The quantitative analysis includes data from 13 studies. The results suggested that occupational silica, pesticides, mould/mildew and World Trade Center ((WTC) dust exposures were associated with increased odds of pulmonary sarcoidosis. Sensitivity analysis confirmed the robustness of the association for WTC dust and mould, whereas the risk associated with silica appeared attenuated in high-quality studies. However, gold exposure was identified as a protective factor. CONCLUSION: Pulmonary sarcoidosis is associated with occupational silica, pesticides, WTC dust and mould. Future research should prioritise gene-environment interactions and granuloma mineralogy to refine preventive strategies and disease management.
12 Apr 2026
Read appraisal →BMJ open respiratory research
Parasternal electromyography in participants with mild or moderate chronic obstructive pulmonary disease in primary care: cohort study to assess technical and clinical application
BACKGROUND: Neural respiratory drive (NRD) measurement, reflecting the balance between respiratory muscle load and capacity, is quantified using surface parasternal electromyography (EMGpara). EMGpara tracks recovery from severe exacerbations of chronic obstructive pulmonary disease (COPD). Among stable COPD participants, we hypothesised the existence of a relationship between NRD, breathlessness and airway obstruction. STUDY AIMS: (1) assessing the feasibility of measuring EMGpara in COPD participants with forced expiratory volume in 1 s (FEV1) ≥50% predicted in primary care; (2) investigating relationships between NRD measures, self-reported breathlessness, airflow obstruction severity and health-related quality of life (HRQoL). METHODS: Participants with stable mild/moderate COPD, using inhaled corticosteroid (ICS) therapy, were recruited from 20 general practices. Participants were randomly allocated to continue using ICS (maintenance group) or to withdraw ICS (withdrawal group) over 6 weeks. EMGpara, spirometry, self-reported breathlessness (modified Borg dyspnoea scale), COPD Assessment Test and Chronic Respiratory Disease Questionnaire Self-Administered Standardised were measured at baseline, 3- and 6-month follow-up. Bland-Altman plots examined agreement between serial measurements. RESULTS: Forty COPD participants were recruited: age 70±9.2 years; body mass index 26±5.3 kg/m2; FEV1 1.74±0.54 L; and FEV1% pred 69.6±14.0%. High-quality EMGpara data were obtained from 35 participants at baseline and 31 participants on three occasions. High intra-rater and inter-rater agreement for EMGpara (intraclass correlation coefficient >0.9) and moderate correlation between EMGpara and FEV1% predicted (r=-0.42; p=0.01) were recorded. No correlation was observed between resting EMGpara and breathlessness or HRQoL measures across the three time points. CONCLUSIONS: EMGpara measurement is feasible in primary care. In this group of COPD patients, lung function was stable across the three time points and EMGpara was associated with the degree of airflow obstruction. In the resting stable state in mild/moderate disease, there was no association between EMGpara and participant-reported outcomes. Further work should investigate the utility of EMGpara in mild/moderate COPD participants during acute exacerbation and recovery.
12 Apr 2026
Read appraisal →BMJ open respiratory research
What is the evidence for virtual wards or hospital-at-home care pathways for exacerbations of chronic obstructive pulmonary disease? A systematic review and meta-analysis.
OBJECTIVES: Given increasing interest in admission avoidance, we evaluated the evidence to support virtual wards (VW) and hospital at home (HaH) models of care during exacerbations of chronic obstructive pulmonary disease (ECOPD). DESIGN: A systematic review and meta-analysis. A comprehensive search of MEDLINE (1946 to March 2024), Embase (1974 to March 2024) and CENTRAL (searched 22 March 2024) was conducted. Risk of bias and a random effects meta-analysis were performed. POPULATION: Adults with an ECOPD presenting to the hospital or who require hospital-led care. INTERVENTIONS: VW: defined as assessments and interventions delivered remotely or HaH (defined as assessments and interventions delivered by healthcare professionals in patient's homes) care pathways, compared with hospital admission. PRIMARY AND SECONDARY OBJECTIVES: Safety (mortality rate of all causes, in-patient, 7 days and 30 days) and readmission rate in 7 and 30 days. Length of stay in hospital and changes in pulmonary function tests. RESULTS: One study assessed VWs (reported in two publications) and 10 assessed HaH. There were no changes in survival or short-term readmission rates attributable to the interventions and no evidence that VW or HaH care pathways reduced the total time a patient spent under hospital-led care, whether at home or in the hospital. CONCLUSIONS: More evidence is needed to support the widespread roll-out of HaH and especially VW pathways for ECOPD. PROSPERO REGISTRATION NUMBER: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024517565.
12 Apr 2026
Read appraisal →PloS one
Prevalence and risk factors for AMS: A systematic review and meta-analysis
BACKGROUND: Acute Mountain Sickness (AMS) is a high-altitude-specific condition with variable prevalence and poorly defined risk factors. Despite growing global exposure to high-altitude environments, no systematic review has comprehensively synthesized AMS epidemiology. OBJECTIVES: To assess the prevalence of AMS and critically evaluate evidence on risk factors associated with increased susceptibility. DESIGN: A systematic review and meta-analysis following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. DATA SOURCES: PubMed, Cochrane Library, Web of Science, CINAHL Plus, China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database (CBM), and Weipu Database (VIP) were comprehensively searched for observational studies investigating the prevalence and risk factors of AMS from January 1, 2004 to December 31, 2024. REVIEW METHODS: Original journal articles were included which met the inclusion criteria. The quality of the included studies was evaluated independently by two investigators. Meta-analysis was conducted using R software (v4.2.2), with estimates of AMS from pooled using a random-effects model. RESULTS: Fifty-eight studies (n = 2,705) were included. The pooled AMS prevalence was 48.25% (95% CI: 42.58-53.96%), with substantial heterogeneity (I² = 82.3%). Significant risk factors included extreme altitude (>5500 m; RR = 1.89), rapid ascent (<6 h; RR = 1.60), marked heart rate increase (>20 bpm; RR = 2.35), and oxygen saturation decline (>10%; RR = 2.02). Prior AMS history (RR = 1.36) and male sex (RR = 1.15) were also associated with higher risk, while older age (>50 years) was protective (RR = 0.78). CONCLUSION: AMS affects nearly half of high-altitude visitors. Altitude, ascent rate, cardiopulmonary response, and prior AMS history are key risk determinants. These findings support pre-exposure risk stratification, staged ascent protocols, and real-time physiological monitoring. PROSPERO CRD42024595365.
11 Apr 2026
Read appraisal →PloS one
Association between hypertension and impaired lung function among adults: A systematic review and meta-analysis
BACKGROUND: Impaired lung function and hypertension are both significant global public health concerns, and their potential association has recently drawn considerable interest. This systematic review and meta-analysis aims to investigate the association between impaired lung function and hypertension, and vice versa. METHODS: We searched CINAHL (EBSCOhost), MEDLINE (PubMed), Scopus and Web of Science up to 22nd July 2025 for observational studies involving adults (≥ 18 years) reporting hypertension and impaired lung function. Grey literature was also searched, and data extraction was verified independently. The review was registered with PROSPERO (CRD42023427631). Random effects models estimated pooled ORs with 95% CIs. Subgroup analysis and meta-regression were performed to identify potential sources of heterogeneity. Quality assessment was conducted using Newcastle-Ottawa Scale and JBI checklist and publication bias were appraised. RESULTS: This systematic review included 28 articles, of which 26 articles were selected for the meta-analysis. Of these, 17 studies (n = 134823) examined impaired lung function as the exposure, while 9 studies (n = 12522) investigated hypertension as the exposure. Our findings revealed that the pooled unadjusted effect of lung function impairment on hypertension was 1.70 (95% CI: [1.53-1.88]), while the pooled adjusted effect was lower at 1.40 (95% CI: [1.31-1.49]). In contrast, the pooled unadjusted effect of hypertension on lung impairment was 3.00 (95% CI: [1.86-4.82]), but the pooled adjusted effect decreased to 1.94 (95% CI: [1.51-2.50]). The findings indicate that obstructive, restrictive and mixed lung function impairments are significantly associated with hypertension. Restrictive lung impairment consistently shows a stronger association with hypertension compared to obstructive impairment when impaired lung function is the exposure. CONCLUSION: This study concludes that there is a significant positive association between impaired lung function and hypertension in both directions, with stronger temporal evidence suggesting that impaired lung function may precede hypertension, while the reverse causal direction lacks such evidence.
11 Apr 2026
Read appraisal →BMJ (Clinical research ed.)
Non-drug perioperative interventions to reduce postoperative pulmonary complications after abdominal surgery: systematic review and meta-analysis
OBJECTIVE: To evaluate the effectiveness of perioperative non-drug interventions in reducing postoperative pulmonary complications (PPCs) in adults undergoing abdominal surgery. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Ovid MEDLINE, Embase, and Web of Science from database inception to January 2025 and updated in January 2026, with no language restrictions. STUDY SELECTION: Randomised controlled trials assessing the effectiveness of perioperative non-drug interventions for the prevention of PPCs in adults undergoing elective abdominal surgery under general anaesthesia, with clearly defined PPCs. MAIN OUTCOME MEASURES: The primary outcome was the proportion of patients developing PPCs. Secondary outcomes included the proportion of patients with PPC subtypes according to European Perioperative Clinical Outcome definitions (respiratory infection, respiratory failure, pleural effusion, atelectasis, or pneumothorax) and hospital length of stay. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies, extracted data, and assessed risk of bias with the Cochrane RoB 2.0 tool. Data were synthesised using meta-analyses and trial sequential analyses, with the evidence certainty assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. RESULTS: 255 trials including 55 260 participants were included, evaluating 10 types of interventions with 39 subtypes for PPC prevention. PPCs occurred in 6467 (11.7%) participants across all included trials. High certainty evidence showed that low fraction of inspired oxygen (FiO2) significantly reduced PPCs (risk ratio 0.81, 95% confidence interval 0.71 to 0.92). Moderate certainty evidence showed benefit for four intervention types: lung protective ventilation (risk ratio 0.66, 0.57 to 0.76), physiotherapy (0.55, 0.46 to 0.65), analgesia (0.73, 0.64 to 0.84), and nutrition (0.74, 0.63 to 0.87), with individualised positive end expiratory pressure, composite lung protective ventilation, early mobilisation, and epidural analgesia also showing benefit at the subtype level. Trial sequential analysis confirmed sufficient cumulative evidence for all the above interventions except early mobilisation. By contrast, goal directed haemodynamic therapy, targeted blood pressure management, restrictive fluid therapy, and postoperative bi-level positive airway pressure showed no evidence of benefit, with moderate certainty. CONCLUSIONS: This synthesis establishes an evidence hierarchy for PPC prevention in abdominal surgery. Low FiO2 is the only intervention supported by high certainty evidence and should be prioritised in clinical practice. Other beneficial strategies include lung protective ventilation, physiotherapy, analgesic techniques, and nutrition interventions. Conversely, the role of goal directed haemodynamic therapy-despite its widespread use-warrants reconsideration for PPC prevention. These findings facilitate prioritisation of effective interventions and development of evidence based guidelines. STUDY REGISTRATION: PROSPERO CRD42025637449.
11 Apr 2026
Read appraisal →Annals of medicine
Comparison of VCV and PCV-VG modes on diaphragmatic function in diabetic patients undergoing laparoscopic colorectal surgery: a prospective randomized controlled study
BACKGROUND: Diabetic patients are prone to induce diaphragmatic weakness, which can lead to postoperative pulmonary complications (PPCs). The optimal mechanical ventilation mode may potentially improve postoperative diaphragmatic function. This study evaluates the effects of two ventilation modes under driving pressure-guided ventilation strategy on diaphragmatic function, as assessed by diaphragm thickening fraction (DTF) and diaphragm excursion (DE), in diabetic patients following laparoscopic colorectal surgery. METHODS: Eighty patients diagnosed with Type II diabetes scheduled for elective laparoscopic colorectal surgery, were randomly allocated to either the pressure-controlled volume-guaranteed ventilation (PCV-VG) group (Group P) or the volume-controlled ventilation (VCV) group (Group V) during surgery. The primary outcome was diaphragmatic function assessed during both tidal breathing and maximal inspiratory effort after surgery. Secondary outcomes included intraoperative mechanical power, PPCs, and other complications. RESULTS: A total of eighty patients were included in the final analysis. The averaged area under the curve (AUC) for mechanical power during ventilation was significantly lower in Group P than in Group V (p = 0.002). PCV-VG significantly improved both DE and DTF within the first two days post-surgery (AUCDEtidal: p = 0.088, AUCDTFtidal: p = 0.004, AUCDEmax: p = 0.029, AUCDTFmax: p = 0.017). Postoperative diaphragmatic weakness was less frequent in Group P than in Group V (p = 0.019). However, there was no difference in the incidence of PPCs between the two groups (p = 0.155). CONCLUSION: PCV-VG mode can reduce intraoperative mechanical power, better preserve postoperative diaphragmatic function. However, these improvements did not translate into clinical benefits, as evidenced by the lack of reduction in the incidence of PPCs.
10 Apr 2026
Read appraisal →Microbiology spectrum
PneumoWiki: a pan-genome-based database for the pathogen Streptococcus pneumoniae
UNLABELLED: The Gram-positive bacterium Streptococcus pneumoniae is a major human pathogen that exhibits more than 100 different capsular serotypes and considerable genomic variability. S. pneumoniae is also an important model organism for basic and biomedical research, with a number of strains with different characteristics being used. To address this, we have created the manually curated pan-genome database PneumoWiki (https://pneumowiki.med.uni-greifswald.de) that integrates genomic data of 43 S. pneumoniae strains with various aspects of functional annotation. All data relating to a specific gene and gene product are compiled on a Gene page. In addition to the gene and protein sequences and annotation from NCBI RefSeq, this includes, for example, data on gene expression, protein function, and localization. The information is supplemented with links to the data sources as well as to other databases and relevant literature. The basic concept of PneumoWiki is the interlinked presentation of pan-genome-based and strain-specific information. Therefore, register tabs with strain names at the top of the Gene pages allow switching between orthologous genes and the corresponding pan-genome page. The pan-genome gene pages contain a summary with functional information and the occurrence of the gene, an ortholog table for the 43 S. pneumoniae strains, a multiple-strain genome viewer as well as a protein sequence alignment. PneumoWiki was developed as a user-friendly tool and is available free of charge. The data collected in PneumoWiki are accessible via various download options. Overall, PneumoWiki is a resource for the research community that supports the analysis and interpretation of experimental data and, in particular, enables the integration of the knowledge available for different S. pneumoniae strains. IMPORTANCE: Databases that integrate and curate the large amounts of genomic data and functional information available for well-studied organisms play an increasingly important role in many areas of life sciences, including infection research. Streptococcus pneumoniae is a major human pathogen associated with high morbidity and mortality rates. The species exhibits high variability in its genome sequence, and a number of strains with different characteristics are used in pneumococcal research. We created the wiki-type database PneumoWiki to provide researchers with curated, up-to-date information on a range of pneumococcal genomes, focusing on the individual genes and encoded proteins and their functional annotation. The key feature of PneumoWiki is the pan-genome approach, which allows combining information of individual strains, thereby supporting the analysis of experimental data and comparisons between studies using different strains. In this way, the database becomes a valuable research tool that can contribute to a better understanding of pneumococcal physiology.
10 Apr 2026
Read appraisal →European journal of pediatrics
Randomised crossover trial of neurally adjusted ventilatory assist (NAVA) for neonates with congenital diaphragmatic hernias: the NAN-C study
Retrospective studies comparing NAVA to assist control ventilation (ACV) in neonates with congenital diaphragmatic hernia (CDH) have shown that ventilatory mode may improve respiratory parameters. The aim of this study is to determine if infants with CDH studied post-operatively had a lower oxygenation index (OI) on NAVA compared to ACV. This dual-centre randomised cross-over trial compared post-operative NAVA with ACV in infants with CDH. Infants were randomised to receive either NAVA or ACV first in a 1:1 ratio for a 4-h period. At the end of each 4-h period, blood gas analysis was performed and the OI calculated. The inspired oxygen concentration (FiO2), the peak inflation (PIP), and mean airway pressure (MAP) were averaged from the last 5 min on each mode. Eleven infants were randomised. Nine infants completed the trial. with median gestational age of 38 (range 34.6-39.3) weeks and median postnatal age of 7 (range 5-36) days. Eight had left-sided CDH, six had patch repair and two had thoracoscopic repair. The mean OI after 4 h on NAVA was 3.9 ± 1.8 compared to 5.9 ± 1.61 on ACV (p = 0.008). The peak Edi (6.05 ± 4.5 versus 9.86 ± 7.3 µV, p = 0.028), PIP (17 ± 6.3 versus 22 ± 5.3 cmH2O, p = 0.017), and MAP (8.7 ± 2.6 versus 11.1 ± 1.8 cmH2O, p = 0.008), expiratory tidal volume (5.06 ± 0.71 versus 9.86 ± 1.29 ml/kg, p = 0.043) were lower on NAVA versus ACV. Two infants were randomised, but the trial was stopped due to a low Edi signal.Conclusion: NAVA compared to ACV improved oxygenation postoperatively in infants with CDH. On NAVA, infants had lower oxygen indices, peak Edi, expiratory tidal volume and peak and mean airway pressures.
10 Apr 2026
Read appraisal →Scientific reports
Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model
To improve automatic lung nodule detection in chest X-ray images, this study proposes an improved YOLOv12-based detection framework by integrating space-to-depth convolution (SPDConv), a dynamic upsampling module (DySample), and a lightweight feature aggregation module (VoVGSCSP). SPDConv enhances spatial information preservation during feature extraction, DySample replaces conventional upsampling to improve multi-scale feature fusion, and VoVGSCSP strengthens feature representation while reducing computational redundancy. The optimized YOLOv12-DSV model was trained and evaluated using a publicly available chest X-ray dataset with annotated lung nodules from the Roboflow platform, with performance assessed through five-fold cross-validation and external testing. Experimental results show that the proposed model achieved an mAP50 of 0.735 and an mAP50-95 of 0.426, outperforming the original YOLOv12 model (mAP50: 0.704; mAP50-95: 0.411). In addition, the proposed model reduced the number of parameters from 2.52 to 2.21 M, decreased FLOPs from 6.0 to 5.2 G, and increased inference speed from 97.6 to 107.8 FPS. These results indicate that the proposed YOLOv12-DSV model improves detection accuracy while reducing computational cost, achieving a more favorable balance between detection performance and model complexity for lung nodule localization in chest X-ray images.
10 Apr 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Prevalence of respiratory viruses in stable and acute asthma: a systematic review and meta-analysis
BACKGROUND: Respiratory viruses, frequently detected in asthma, are associated with worse outcomes. This meta-analysis systematically quantifies the prevalence of respiratory viruses in stable and acute asthma, across children and adults, and explores factors associated with increased viral burden through meta-regression. METHODS: This prospectively registered meta-analysis (PROSPERO-CRD42023375108) included studies employing molecular techniques to assess respiratory virus prevalence in asthma. Three databases were searched in August 2024. Risk of bias and certainty of evidence were assessed. We performed random-effects meta-analysis of proportions. RESULTS: We included 111 eligible studies. Moderate-certainty evidence indicated a pooled prevalence of any respiratory virus of 33.9% (95% confidence interval 24.8-43.7%) in children and 23.0% (12.9-35.0%) in adults with stable asthma. In acute asthma, prevalence increased to 58.8% (52.5-65.0%) in children and 49.9% (41.2-58.5%) in adults (moderate certainty). Rhinovirus was the most frequently identified virus, especially in acute asthma (45.0% in children versus 21.2% in adults). Respiratory syncytial virus and bocavirus were more common in younger children, while coronavirus and influenza were more frequently detected in adults; respiratory syncytial virus peaked in older adults too. A higher prevalence of influenza virus B and adenovirus in children, and of influenza virus A and parainfluenza 2 in adults with severe versus non-severe acute asthma suggests a potential association with more severe acute attacks. CONCLUSION: Respiratory viruses are common in both stable and acute asthma. This suggests that the diagnostic value of a positive viral test during acute episodes may be limited and could benefit from complementary biomarkers to improve interpretation.
10 Apr 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
The burden of autoimmune pulmonary alveolar proteinosis: a systematic review
BACKGROUND: Autoimmune pulmonary alveolar proteinosis (aPAP) is characterised by abnormal alveolar surfactant accumulation and reduced pulmonary gas transfer. Disease severity and progression depend on pulmonary surfactant accumulation, the rate of which varies widely among patients. Currently, whole-lung lavage (WLL) is the most widely accepted therapy. This review addresses the burden of aPAP on patients, caregivers and society. METHODS: MEDLINE and Embase databases were systematically searched for reports on the manifestations, treatment burden, caregiver impact and healthcare costs of aPAP published after 2000. RESULTS: Out of 1023 publications identified, 50 reported relevant data (for 2855 aPAP patients), including 43 observational studies and seven phase 2/3 trials. Commonly reported symptoms included dyspnoea, cough and sputum production. Clinical manifestations included progressive hypoxaemia, reduced exercise capacity, reduced quality of life, and an increased rate of serious infections. Low prevalence and nonspecific signs and symptoms contributed to delayed diagnosis of aPAP, frequent misdiagnoses, use of multiple tests with nondiagnostic results, and therapies that were inappropriate or exacerbated the disease. WLL was the most frequently administered therapy, and many patients required repeat procedures. Medical care costs were higher for PAP patients than for non-PAP control patients. CONCLUSIONS: The results highlight the multifactorial and substantial burden of aPAP on patients. Significant unmet needs remain, particularly in achieving timely and accurate diagnosis and in providing effective, well-tolerated therapies that address the underlying pathophysiology of the disease.
10 Apr 2026
Read appraisal →The New England journal of medicine
Setidegrasib in Advanced Non-Small-Cell Lung Cancer and Pancreatic Cancer
BACKGROUND: The KRAS p.G12D variant occurs in 5% of patients with non-small-cell lung cancer (NSCLC) and is the most common substitution variant in pancreatic ductal adenocarcinoma, occurring in 40% of patients, but no targeted therapies directed against this variant are currently approved for clinical use. Setidegrasib (ASP3082) is a first-in-class KRAS G12D-targeted protein degrader. METHODS: We conducted this phase 1 study to evaluate the safety, pharmacokinetics, pharmacodynamics, and antitumor activity of setidegrasib in patients with previously treated advanced solid tumors harboring KRAS p.G12D variants. The primary objectives were to evaluate the safety profile, as indicated by dose-limiting toxic effects and adverse events (the primary end points), and to determine the phase 2 dose. Setidegrasib was administered intravenously once weekly at doses of 10 to 800 mg. RESULTS: Overall, 203 patients were enrolled. Among the 76 patients who received setidegrasib at a dose of 600 mg, which was ultimately selected as the phase 2 dose, adverse events occurred during treatment in all the patients, with events of grade 3 or higher in 42%. Treatment-related adverse events occurred in 93% of the patients; the most common were transient infusion-related reactions (in 80%) and nausea (in 30%). Adverse events led to discontinuation in 2 patients. Among the 45 patients with NSCLC who received the 600-mg dose, 36% (95% confidence interval [CI], 22 to 51) had a partial response, the median progression-free survival was 8.3 months (95% CI, 4.1 to could not be estimated), and the estimated 12-month overall survival was 59% (95% CI, 40 to 74). Among the 21 patients with metastatic pancreatic ductal adenocarcinoma who received the 600-mg dose as second- or third-line treatment (of whom 67% received setidegrasib as third-line treatment), 24% (95% CI, 8 to 47) had a response, the median progression-free survival was 3.0 months (95% CI, 1.4 to 6.9), and the median overall survival was 10.3 months (95% CI, 4.2 to 13.0). CONCLUSIONS: Setidegrasib was associated with antitumor activity and a low incidence of treatment discontinuation due to adverse events in patients with previously treated advanced KRAS p.G12D-mutated NSCLC or pancreatic ductal adenocarcinoma. (Funded by Astellas Pharma; ClinicalTrials.gov number, NCT05382559.).
9 Apr 2026
Read appraisal →Microbiology spectrum
Evaluation of contezolid's cerebrospinal fluid concentration and safety in tuberculous meningitis patients
Tuberculous meningitis (TBM) is the most severe form of tuberculosis (TB) with high mortality. This study evaluated the cerebrospinal fluid (CSF) concentration and safety of the novel oxazolidinone contezolid compared to linezolid in TBM patients. In this randomized prospective study, 10 TBM patients received either a linezolid-containing (n = 5) or contezolid-containing (n = 5) anti-TB regimen. CSF and blood concentrations were measured at 2 and 6 h post-dose. Peak (2 h) and trough (6 h) concentrations, area under the concentration-time curve (AUC), and safety were assessed. Contezolid CSF concentrations exceeded the Mycobacterium tuberculosis (Mtb) MIC (0.5 μg/mL) at 2 h (median: 1.0806 μg/mL, range: 0.9295-1.3165 μg/mL) but declined by 6 h (median: 0.7920 μg/mL, range: 0.1867-1.0194 μg/mL). Linezolid CSF concentrations were significantly higher than contezolid at both 2 h (median: 3.251 μg/mL, range: 1.9545-4.9636 μg/mL; P = 0.008) and 6 h (median: 1.623 μg/mL, range: 0.941-1.765 μg/mL; P = 0.016), remaining above MIC in all samples at 6 h. The mean CSF AUC for contezolid (4.637 μg·h/mL, 95% CI: 3.599-5.675) was significantly lower than that for linezolid (12.537 μg·h/mL, 95% CI: 7.8797-17.277; P = 0.008). Blood concentrations were higher than CSF for both drugs at all time points. No serious drug-related adverse events occurred. Contezolid effectively penetrates the blood-CSF barrier in TBM patients, achieving CSF concentrations above the MIC for Mtb. Although its CSF exposure was significantly lower than linezolid, its demonstrated penetration and safety profile suggest contezolid warrants further investigation as a potential treatment option for drug-resistant TBM.IMPORTANCETuberculous meningitis (TBM) is the deadliest form of tuberculosis, especially difficult-to-treat drug-resistant TBM. Finding new, effective, and safe medicines is critical. This study provides evidence in TBM patients that a newer antibiotic, contezolid, successfully reaches the infection site in cerebrospinal fluid (CSF) at levels expected to kill Mycobacterium tuberculosis. While linezolid achieved higher levels in CSF, contezolid still reached concentrations predicted to be effective and caused no serious side effects in our study. This is important because contezolid might offer a safer alternative to linezolid, which can have significant long-term toxicity. These promising results suggest contezolid could become a valuable new weapon against refractory drug-resistant TBM, potentially saving lives where current options are limited or too toxic.
9 Apr 2026
Read appraisal →Biomedical physics & engineering express
Bluetooth low energy thermohygrometric sensor for continuous respiratory rate monitoring: 48 h breathing simulator and human proof-of-concept
Continuous respiratory rate monitoring is essential for early detection of clinical deterioration, but conventional methods like capnography are often impractical outside high-acuity settings. We developed a low-cost, Bluetooth Low Energy (BLE) respiratory rate monitor incorporating a MEMS temperature-humidity sensor (AHT21) and Nordic nRF52840 microcontroller, designed to attach externally to a standard oxygen mask. The device detects breaths using rapid, cyclic changes in exhaled temperature and humidity via a slope-based time-domain algorithm. Bench validation over 48 h using a breathing simulator (5-60 breaths min-1, tidal volume 300 ml, 34 °C, 95%-100% RH) demonstrated 96.4% overall breath detection accuracy and strong correlation with programmed rates (r = 0.996, P < 0.0001). Sensor saturation occurred at high respiratory rates (60 breaths min-1), and high ambient humidity levels (90% RH) both of which reduced measurement accuracy. Human testing in three healthy adults showed strong agreement with capnography (mean bias 0.44 breaths min-1; 95% limits of agreement -1.86 to 2.75), with minor under-detection at the highest rate (50 breaths min-1). The device operated reliably across oxygen flow rates of 4-15 l min-1, with reduced performance at 0-2 l min-1due to saturation of the sensor. These results demonstrate that BLE-enabled MEMS thermohygrometric sensors can accurately monitor respiratory rate under controlled conditions while highlighting operational limits due to sensor saturation. This study extends prior work on humidity-based respiratory monitoring and illustrates the potential for low-cost, portable respiratory rate devices suitable for low-acuity or resource-limited clinical settings.
9 Apr 2026
Read appraisal →Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Subjective and objective measures of sleep-related function from the Cardiovascular Endpoints For Obstructive Sleep Apnea with Twelfth Cranial Nerve Stimulation (CARDIOSA-12) trial : Sleep-related functional outcomes in CARDIOSA-12.
PURPOSE: The effect of therapeutic versus partially therapeutic levels of hypoglossal nerve stimulation (HGNS) therapy on cognitive measures and patient-reported outcomes (PROs) was evaluated in patients with moderate-severe obstructive sleep apnea (OSA). METHODS: In a 10-week, double-blind, randomized crossover therapy trial, subjects optimized on HGNS therapy underwent 4 weeks each of active and control (partially therapeutic) HGNS therapy. Cognitive measures (psychomotor vigilance test [PVT], digit symbol substitution test [DSST]) and sleep-related PROs were assessed after each 4-week treatment period. RESULTS: Sixty subjects were randomized and completed the primary study protocol, including PROs. A subset of randomized subjects (n = 43) underwent cognitive testing. This subset was, on average, older (65.7 ± 10.2 years), overweight/obese (body mass index [BMI] 29.7 ± 4.6 kg/m2), and had severe OSA at baseline (apnea-hypopnea index [AHI] 34.2 ± 15.0 events/h). There were no differences in cognitive performance measures between active and control (partially therapeutic) HGNS conditions in all subjects; in the per-protocol analysis, however, improvement in DSST reaction time with active HGNS was demonstrated (mean [95% CI] change on active therapy = - 153.6 [- 285.3, - 22.0] ms; p = 0.025). Significant improvements in all PROs (Epworth Sleepiness Scale, Insomnia Severity Index, Functional Outcomes of Sleep Questionnaire, and Snoring Visual Analog Scale) were observed in the active HGNS condition. CONCLUSIONS: In this secondary analysis of the CARDIOSA-12 randomized crossover trial of subjects using HGNS, subjective PROs, but not objective cognitive measures, were improved with active HGNS compared to control (partially therapeutic) HGNS therapy. These findings warrant additional investigation examining the relationship between subjective and objective neurocognitive outcomes in OSA. The effect of hypoglossal nerve stimulation (HGNS), a novel and promising therapy for obstructive sleep apnea (OSA), on neurocognitive deficits remains underexplored. This study evaluated whether HGNS therapy improves cognitive performance using the psychomotor vigilance test (PVT) and digit symbol substitution test (DSST), along with patient-reported outcomes (PROs) related to sleepiness, snoring, insomnia, and sleep-related function. In the modified intention-to-treat analysis, no significant differences in PVT or DSST outcomes were observed between active and control (partially therapeutic) HGNS therapy; however, in a subset of participants with ≥ 50% reduction in AHI with active HGNS, improvement in DSST reaction time was noted. All PROs significantly improved with active HGNS, suggesting benefits in subjective measures without substantial changes in objective cognitive measures.
9 Apr 2026
Read appraisal →Environmental science & technology
Molecular Representation and Closed-Loop Validation for Toxicity Assessment of Organic Compounds in Ambient Air PM2.5
Although the health impacts of fine particulate matter (PM2.5) are primarily attributed to its chemically diverse composition rather than to mass concentration, assessing the toxicity of PM2.5 constituent compounds remains highly challenging due to chemical complexity and limited experimental scalability. This study introduces an interpretable machine learning (ML) framework using a curated A549 cytotoxicity data set (19,841 compounds) that integrates customized Molecular Access System (MACCS) fingerprints, six base models, and a stacking ensemble meta-model, all optimized with a biobjective strategy to assess the toxicity of organic compounds in PM2.5. The stacking ensemble model demonstrated satisfactory performance (test AUC > 0.8), exhibiting good generalization, adaptability, and robustness. Nontargeted analysis generated and experimentally validated a prediction set from the Hong Kong PM2.5 samples (51 of 387 compounds confirmed from 13 classes), demonstrating broad applicability on an independent Nanjing PM2.5 set (572 compounds). Key substructures driving toxicity, identified through a SHapley Additive exPlanations (SHAP) analysis and cell experimental validation, revealed that PM2.5 compounds with aromatic rings and nitrogen-based functional groups (e.g., aromatic α,β-unsaturated ketones, aromatic amines, aromatic nitro compounds, and tertiary amines) likely contribute to high toxicity. The derived "structure-toxicity" rules narrow the search space from thousands of compounds to those containing critical substructures, enabling efficient prioritization of toxic components and providing a foundation for improving model specificity and predictive accuracy in future studies.
8 Apr 2026
Read appraisal →Pulmonology
Low-dose theophylline in mild to moderate Chronic Obstructive Pulmonary Disease: A randomized clinical trial in China
BACKGROUND: Low-dose theophylline has anti-inflammatory effects, but its efficacy in mild to moderate COPD remains controversial. RESEARCH QUESTION: Is low dose theophylline effective and safe in patients with mild to moderate COPD? METHODS: In this prospective, parallel-group, randomised, double-blind, placebo-controlled trial across ten centres in China, subjects with mild to moderate COPD were randomized 2:1 to oral low-dose theophylline sustained-release tablets (100 mg twice daily) or matching placebo for 12 months. Primary endpoints were between-group differences in pre-bronchodilator FEV1 change from baseline to 12 months and annual exacerbation rate. FINDINGS: 734 subjects were randomized; 615 completed the study. Compared with placebo, low-dose theophylline significantly ameliorated FEV1 before bronchodilator use by 30 ml [95% CI, 3 to 57 ml] (p = 0.031) at month 12, with a between-group difference in mean change from baseline of 28 ml/year [95% CI, -2 to 58 ml/year] (p = 0.063). The annual exacerbation rate did not differ between groups (0.46 ± 0.03 vs 0.43 ± 0.04 per patient-year; RR, 1.07 [95% CI, 0.84 to 1.36]; p = 0.607). In addition, the incidence of adverse events was comparable between both groups. INTERPRETATION: In mild to moderate COPD, low-dose theophylline was well-tolerated but did not reduce exacerbation rates. A modest improvement in pre-bronchodilator FEV1 was observed, though its magnitude was limited. Larger trials are needed to further evaluate its potential to improve lung function in this population. What is already known on this topic:The efficacy of low-dose theophylline specifically in the mild to moderate COPD population remains unclear.What this study adds:Our study indicated that low-dose theophylline treatment alone was associated with a significant improvement in pre-bronchodilator FEV1 and demonstrated good tolerability in patients with mild to moderate COPD.How this study might affect research, practice or policy:What we found in this study presented promising opportunities for intervention with theophylline alone in patients with mild to moderate COPD.
8 Apr 2026
Read appraisal →Pulmonology
Comparative diagnostic performance of advanced endoscopic techniques in suspected sarcoidosis: A systematic review and network meta-analysis
BACKGROUND: While various endoscopic techniques are used for minimally invasive tissue sampling in suspected sarcoidosis, comprehensive evidence comparing their diagnostic efficacy is limited, and the optimal approach remains unclear. RESEARCH QUESTION: Which endoscopic technique provides the greatest diagnostic advantage for sarcoidosis? STUDY DESIGN AND METHODS: We systematically searched 3 databases for studies published up to 22 April 2025. A network meta-analysis was conducted within a Bayesian framework, with diagnostic yield as the primary outcome. RESULTS: A total of 35 studies were included. Compared to TBLB, both EBUS-guided intranodal forceps biopsy (EBUS-IFB) (RR 3.50, 2.14-5.84) and EBUS-guided transbronchial mediastinal cryobiopsy (EBUS-TMC) (RR 3.52, 1.97-6.48) showed higher diagnostic yield. SUCRA rankings placed EBUS-IFB (0.912) and EBUS-TMC (0.910) at the top, followed by EBUS-guided core needle biopsy (EBUS-CNB) (0.587), EUS-FNA (0.582), and EBUS-TBNA (0.560), EUS-B-FNA (0.498). Conventional techniques, including cTBNA (0.279), TBLB (0.168), and EBB (0.001), ranked lowest. All procedures had a favourable safety profile. CONCLUSIONS: In centres with appropriate expertise, EBUS-IFB and EBUS-TMC may offer superior diagnostic performance for sarcoidosis. However, given the low certainty of evidence, these conclusions should be interpreted with caution. Clinical decisions should be individualised, considering the patient's condition when determining the final diagnostic strategy.
8 Apr 2026
Read appraisal →Medicine
Adverse events associated with the use of nivolumab in the middle-aged and older population (>45 years): A pharmacovigilance analysis based on the FAERS database.
The clinical use of nivolumab has significantly improved the prognosis for survival in patients with a wide range of advanced malignancies. Given the increased incidence of tumors, there is a need to gain insight into the true extent of adverse events (AEs) associated with the nivolumab drug in the middle-aged and elderly population. This pharmacovigilance study was based on an analysis of reports from the US Food and Drug Administration's adverse event reporting system for the period January 1, 2014 to December 31, 2024, and compared AEs of the drug nivolumab in the middle-aged and elderly populations using proportional reporting ratio, reporting odds ratio, BCPN, and multi-item gamma Poisson shrinker methods. The analyses showed the presence of AEs in a variety of systems, mainly involving several systems such as endocrine disorders, nervous system disorders, skin and subcutaneous tissue disorders. The immune-related AEs listed in the package insert were validated by us and their clinical significance warrants careful consideration to further guide clinical dosing, adjust therapeutic decision-making, and develop age-adapted dosing algorithms for population pharmacokinetic development in order to minimize potential risks to patients. With the exception of reports of an unspecified nature, a greater proportion of the sample was male (65.7%) than female (32.5%), indicating significant variations. The highest percentage of reports was observed in patients aged between 65 and 85 years and in patients with a body mass index ranging from 50 to 100 kg/m2. The primary indication was non-small cell lung cancer. The top 3 AE signals reported with nivolumab were malignant neoplasm progression, death, and pyrexia, with the majority of AE cases occurring within the first month following nivolumab initiation.
5 Apr 2026
Read appraisal →BMJ open respiratory research
Associations between respiratory diseases and lung cancer risk: a secondary analysis of the large-scale prospective UK biobank cohort
INTRODUCTION: Respiratory diseases are significant risk factors for lung cancer; however, the association between acute respiratory infections and lung cancer incidence requires further exploration. METHODS: We performed a secondary analysis of the prospective UK Biobank cohort, including participants aged 37-73 years recruited from 22 assessment centres across the UK between 2006 and 2010. Cox proportional hazards models estimated HRs for incident lung cancer according to respiratory disease status, which was defined based on linked hospital inpatient records. Mediation analysis explored potential biomarkers, and Mendelian randomisation assessed causal relationships. RESULTS: During a mean follow-up of 10.44 years (4 790 738 person-years), 2189 participants developed lung cancer. Among 107 007 individuals with respiratory diseases, 1322 cases occurred (incidence rate 27.6 per 10 000 person-years), compared with 867 cases among 351 876 participants without respiratory diseases (7.9 per 10 000 person-years). Overall, respiratory diseases were associated with increased lung cancer risk (HR 2.97, 95% CI 2.75 to 3.21). Acute respiratory infections, including acute nasopharyngitis (HR 3.41; 95% CI 1.83 to 6.34), influenza (HR 3.90; 95% CI 2.53 to 6.01), viral pneumonia (HR 9.86; 95% CI 6.55 to 14.85) and bacterial pneumonia (HR 6.28; 95% CI 4.40 to 8.96), showed strong associations with lung cancer incidence. In subtype analyses, squamous cell carcinoma exhibited the highest risk elevation (HR 3.65; 95% CI 3.06 to 4.36). Mediation analysis indicated that neutrophil counts partially mediated these associations (proportion mediated up to 8%). CONCLUSION: Acute respiratory infections were associated with higher lung cancer incidence, providing hypothesis-generating evidence that may inform future risk stratification research.
5 Apr 2026
Read appraisal →Journal of special operations medicine : a peer reviewed journal for SOF medical professionals
AI-Assisted Lung Sliding Detection in Point-of-Care Ultrasound by Marine Corps Corpsmen: A Multi-Reader Study
BACKGROUND: Artificial intelligence (AI) has the potential to address training limitations and inter-operator variability that constrain the use of lung ultrasound (LUS) in austere and prehospital settings. This pilot study evaluated whether AI-based decision support could improve the diagnostic accuracy and confidence of United States Marine Corps Corpsmen in identifying absent lung sliding, a key indicator of pneumothorax, during LUS interpretation. METHODS: This pilot-prospective multi-reader, multi-case study involved five military medics, all novices in point-of-care ultrasound, each interpreting 50 de-identified LUS video clips twice, once without AI assistance (control) and once with AI assistance (ATLAS, Deep Breathe Inc., London, Canada), in randomized order with at least a 2-hour washout between sessions. Expert consensus served as a reference standard. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Differences were analyzed using the Random-Reader Random-Case method. Per-clip reader confidence ratings were compared using the Stuart-Maxwell test. RESULTS: AI assistance significantly improved diagnostic performance across all measured outcomes. The mean AUROC increased from 0.72 (SD 0.16) without AI to 0.93 (SD 0.04) with AI (P=.03). Sensitivity rose from 0.63 (SD 0.14) to 0.90 (SD 0.09), specificity from 0.70 (SD 0.15) to 0.86 (SD 0.10), and overall accuracy from 0.67 (SD 0.10) to 0.88 (0.06) (McNemar's test, P<.001). Reader confidence also improved, with high-confidence ratings nearly doubling from 20% to 37%, and low-confidence ratings decreasing from 38% to 33%. These distributional changes were statistically significant (Stuart-Maxwell χ², P<.001). CONCLUSION: AI support markedly improved the diagnostic accuracy and confidence of novice LUS interpretation for detecting absent lung sliding. These findings suggest that real-time AI-based decision support may help improve access to high-quality LUS in military and other resource-limited care settings.
5 Apr 2026
Read appraisal →PloS one
Baseline associations between thoracic expansion, respiratory function, and pain in nonspecific low back pain: A cross-sectional analysis from the DIAFRAGMA trial
OBJECTIVE: To examine the associations of respiratory variables (chest and abdominal expansion, spirometry) with pain, lumbar mobility, and quality of life in nonspecific low back pain (NSLBP) using baseline data from the DIAFRAGMA trial. DESIGN: Cross-sectional study using baseline data from a randomized controlled trial. PARTICIPANTS: Fifty subjects (22 females [44%], 28 males [56%]; mean age 45.96 years; BMI 26.97 kg/m²) with NSLBP recruited between January and July 2024. Main measures: Axillary, sternal, and abdominal inspiratory expansion; spirometric parameters (FVC, FEV1); pain via pressure pain thresholds (PPT), Schober test, FFT; and quality of life via SF-12. METHODS: Pearson correlations assessed relationships between respiratory and clinical variables. Multiple linear regression adjusted for age, sex, and BMI. Significance was set at p < 0.05 with Bonferroni correction. RESULTS: Abdominal expansion correlated with FVC (r = 0.45; p < 0.001), FEV1 (r = 0.39; p = 0.003), PEF (r = 0.28; p = 0.03), axillary (r = 0.47; p < 0.001) and sternal expansion (r = 0.58; p < 0.001), PPT at L4 (r = 0.26; p = 0.04) and L5 (r = 0.29; p = 0.02), and SF-12 (r = 0.25; p = 0.04). BMI correlated negatively (r = -0.31; p = 0.02). In regression, abdominal expansion independently predicted pain after adjustment. CONCLUSIONS: Reduced thoracoabdominal expansion and spirometric performance are associated with greater pain and poorer quality of life in NSLBP, supporting the diaphragm's role in low back pain.
5 Apr 2026
Read appraisal →The European respiratory journal
Impact of positive airway pressure therapy on pulmonary arterial pressures in obstructive sleep apnoea or obesity hypoventilation syndrome: a systematic review and meta-analysis
BACKGROUND: There is limited and conflicting evidence on the impact of obstructive sleep apnoea (OSA) and its treatment with positive airway pressure (PAP) therapy on pulmonary hypertension (PH). Our research question was: does treatment of OSA and obesity hypoventilation syndrome (OHS) with PAP therapy improve pulmonary arterial pressures? METHODS: A systematic review and random effects meta-analysis was performed (PROSPERO: CRD42024595312). PubMed, Embase, Scopus, Web of Science, EBSCO, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov were searched using the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Studies reporting pulmonary arterial pressure in patients with OSA and OHS pre- and post-PAP therapy were included. The primary outcome was post-PAP therapy change in pulmonary arterial pressures, with separate analyses on pulmonary arterial systolic pressure and mean pulmonary arterial pressure. Sensitivity analysis was performed to assess the impact of PAP therapy in patients with only OSA or OHS. RESULTS: 23 studies (20 observational and three randomised controlled trials) with 733 patients were included (19 with OSA (n=518) and four with OHS and concomitant OSA (n=215)). Most patients had severe OSA. PAP therapy was associated with a mean reduction in pulmonary arterial pressure of 5.96 (95% CI 3.20-8.73) mmHg; p<0.0001. In patients with baseline PH (n=233), the reduction was 11.41 (95% CI 8.66-14.15) mmHg; p<0.00001. The beneficial effect of PAP therapy was similar in OSA and OHS. There was no sex difference in the impact of PAP therapy. Heterogeneity was high across studies (I2>85%), reflecting differences in study design, patient populations and methodologies. CONCLUSIONS: PAP therapy reduces pulmonary arterial pressures in patients with mostly severe OSA or OHS, with a more pronounced effect in those with baseline PH. As such, our findings may not apply to patients with milder degrees of OSA. While reductions in pulmonary arterial pressures were observed in all groups, the low certainty necessitates cautious application of these findings in clinical practice.
5 Apr 2026
Read appraisal →BMJ open
Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study
OBJECTIVES: To develop and evaluate an explainable machine learning framework enhanced with synthetic data generation to predict unplanned 30-day hospital readmissions among patients with chronic obstructive pulmonary disease (COPD), heart failure (HF) and type 2 diabetes mellitus (T2DM), and to identify key clinical and social predictors of readmission. DESIGN: A retrospective cohort study using electronic health record data incorporating both structured variables and information extracted from unstructured clinical notes. Synthetic data were generated using advanced resampling and deep learning-based techniques to address outcome imbalance and improve model training. SETTING: Intensive care unit and general ward admissions at a single tertiary academic medical centre included in the MIMIC-IV (Medical Information Mart for Intensive Care IV) database. PARTICIPANTS: Adult patients (≥18 years) were admitted with a primary diagnosis of COPD (n=14 050), HF (n=7097) or T2DM (n=12 735) between 2008 and 2019, with complete 30-day follow-up and no in-hospital mortality during the index admission. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was unplanned all-cause hospital readmission within 30-days of discharge. Predictors were drawn from six domains, including demographics, comorbidities, clinical acuity, therapies, behavioural factors and care continuity. Predictive performance was evaluated using multiple machine learning methods and fivefold cross-validation, with model interpretability assessed using established goal and local explanation approaches. RESULTS: Ensemble-based machine learning models demonstrated the strongest predictive performance across all three disease cohorts. Key predictors of readmission included higher illness severity, greater comorbidity burden, medication non-adherence, gaps in preventive care and limited social support. Models incorporating synthetic data augmentation showed improved discrimination compared with models trained on original data alone. CONCLUSIONS: An explainable synthetic-data driven framework incorporating clinical, behavioural and social data can support prediction of 30-day readmissions among patients with common chronic conditions using routinely available electronic health record data.
5 Apr 2026
Read appraisal →Medicine
Effectiveness of mindfulness-based stress reduction in managing fatigue, anxiety, and depression among non-small cell lung cancer patients receiving surgery and adjuvant chemotherapy: A randomized controlled trial
BACKGROUND: Non-small cell lung cancer (NSCLC) patients undergoing surgery and adjuvant chemotherapy often experience debilitating physical and psychological symptoms, including cancer-related fatigue, anxiety, and depression. Mindfulness-based stress reduction (MBSR) has emerged as a complementary intervention to alleviate these burdens and enhance quality of life, but evidence in NSCLC populations remains limited. METHODS: This randomized controlled trial enrolled 80 NSCLC patients, evenly divided into an MBSR group and a Control group. The MBSR group underwent an 8-week mindfulness program, while the Control group received standard care. Outcomes, including fatigue (Piper Fatigue Scale), anxiety (Self-Rating Anxiety Scale), depression (Self-Rating Depression Scale), self-efficacy (General Self-Efficacy Scale), and mindfulness awareness (Mindful Attention Awareness Scale), were assessed at 4 time points: baseline (before surgery), at the fourth week (before chemotherapy), and at 1 and 3 months post-intervention. Statistical analyses included repeated-measures ANOVA and t tests to evaluate group differences. RESULTS: The MBSR group demonstrated significant improvements across all outcomes compared to the Control group. Fatigue levels in the MBSR group peaked at the fourth week but returned close to baseline levels by 3 months post-intervention, while the Control group maintained elevated fatigue scores (P < .05). Anxiety (Self-Rating Anxiety Scale) and depression (Self-Rating Depression Scale) scores significantly decreased in the MBSR group by the fourth week and continued to improve at subsequent time points (P < .01). Self-efficacy (General Self-Efficacy Scale) and mindfulness awareness (Mindful Attention Awareness Scale) showed consistent improvements in the MBSR group, with significant differences evident at 1 and 3 months post-intervention (P < .01). CONCLUSIONS: Mindfulness-based stress reduction effectively reduced fatigue, anxiety, and depression while enhancing self-efficacy and mindfulness awareness in NSCLC patients undergoing surgery and chemotherapy. These findings support the integration of mindfulness-based interventions into routine care to improve patient well-being during and after treatment.
5 Apr 2026
Read appraisal →Critical care explorations
Physician Gestalt Compared With Artificial Intelligence Model to Predict Intubation in Critically Ill Patients
IMPORTANCE: Accurate prediction of intubation in critically ill patients could enable interventions that improve patient outcomes. However, the performance of intensive care physicians compared with machine learning (ML) models remains unknown. OBJECTIVES: To investigate intensive care physicians' ability to predict the need for intubation within 24 hours and compare their performance against an established ML model, Vent.io. DESIGN, SETTING, AND PARTICIPANTS: This prospective observational study in two ICUs surveyed intensivists to test their ability to predict the need for intubation of adult patients under their care. Physician predictions of intubation were then compared with predictions from Vent.io. MAIN OUTCOMES AND MEASURES: Primary metrics include prediction sensitivity, specificity, and descriptive statistics for both physicians and ML model. Generalized linear mixed models investigated the fixed effect of the predictor (physician vs. Vent.io) on both sensitivity and specificity while accounting for the random effects from different physicians. Similar modeling was used to investigate the relationship between physician confidence and correctness. RESULTS: Overall, physicians were quite confident in their predictions of intubation with a median score of 8 (on a 0-10 point scale, with 0 being not at all confident and 10 being extremely confident) out of the 302 surveys administered. Sensitivity was 0.190 and 0.714 for physicians and Vent.io, respectively. Specificity was 0.960 and 0.673 for physicians and Vent.io, respectively. Generalized linear mixed modeling showed that physician confidence was associated with greater odds of correctly predicting intubation outcome (odds ratio [OR] 1.49; 95% CI, 1.22-1.84; p < 0.001). Vent.io had significantly greater odds of being correct when patients required intubation compared with physicians (OR 18.68; 95% CI, 1.87-186.31; p = 0.013). However, intensive care physicians outperformed Vent.io at correctly predicting when patients did not require intubation (OR 24.80; 95% CI, 13.22-46.52; p < 0.001). CONCLUSIONS AND RELEVANCE: Although predictive performance compared with human experts is promising, Vent.io needs real-time testing in a randomized clinical trial to determine if its deployment can improve clinical outcomes.
4 Apr 2026
Read appraisal →The European respiratory journal
A European multicentre, randomised controlled trial of pirfenidone in bronchiolitis obliterans syndrome after bilateral lung transplantation
BACKGROUND: Chronic lung allograft dysfunction (CLAD) is a major obstacle to improving outcomes after lung transplantation. Bronchiolitis obliterans syndrome (BOS), characterised by progressive decline in forced expiratory volume in 1 s (FEV1) due to fibrotic scarring of the small airways, accounts for most CLAD cases. Pirfenidone, an antifibrotic agent used for idiopathic pulmonary fibrosis, was assessed for treating progressive BOS. METHODS: An investigator-initiated, international, multicentre, randomised, double-blind, placebo-controlled phase II trial was conducted in nine European lung transplant centres. Adults with bilateral lung transplants and progressive BOS were randomised (1:1) to receive pirfenidone 2403 mg·day-1 or placebo with standard care for 26 weeks. The primary end-point was change in FEV1 from baseline to week 26, with imputation for missing values. FINDINGS: From 1 May 2015 to 1 December 2019, 477 patients were screened, and 90 were randomised to pirfenidone (n=48) or placebo (n=42). Both groups showed continued decline in FEV1 from baseline to week 26, with no significant difference in intention to treat (ITT), ITT with imputation, or per-protocol analyses. Secondary end-points (graft loss, death, re-transplantation) were similar between groups. Treatment-related serious adverse events were distributed equally. INTERPRETATION: Pirfenidone did not show superiority over placebo and standard care in this exploratory trial. It cannot be recommended for treating BOS. Further research is needed to explore other treatments for CLAD to improve long-term outcomes after lung transplantation.
4 Apr 2026
Read appraisal →Journal of telemedicine and telecare
Telemedicine and digital health for chronic conditions in pediatrics: A systematic review
BackgroundThe prevalence of chronic diseases in children and adolescents has significantly increased. The COVID-19 pandemic accelerated telemedicine adoption, leading to improved healthcare access and outcomes. Despite its benefits, telemedicine is still not fully integrated into standard care. Previous reviews have been limited in scope. This systematic review aims to provide a comprehensive overview of recent trials on web or app-based telemedicine for chronically ill children and adolescents, identifying interventions and outcomes for future healthcare strategies.MethodsA comprehensive search was conducted on PubMed on November 21, 2023, and updated on January 9, 2025. Results were filtered to include prospective intervention studies involving children and adolescents with chronic conditions and limited to publications from 2017 onwards. A total of 8699 results were retrieved, and 1506 titles were screened for eligibility, resulting in 116 studies included in the review.ResultsThese studies covered 45 different conditions, with the highest numbers for diabetes, asthma, and obesity. Telemedicine interventions varied, including gamification, video meetings, integrated devices, psychological components, symptom tracking, and educational content. Most studies reported high feasibility and patient satisfaction. Clinical outcomes improved in some cases, and none were worse than standard care. The heterogeneity limited direct comparisons and meta-analysis, and small sample sizes in many studies affected the generalizability and statistical significance of the findings.ConclusionsOur study highlights the potential of telemedicine in managing chronic illnesses among children, emphasizing the need for standardized methodologies, larger sample sizes, and continued investment in infrastructure and equitable policies to fully realize its benefits in improving accessibility, convenience, cost savings, and overall health outcomes.
4 Apr 2026
Read appraisal →European radiology
Predictive value of artificial intelligence-based quantitative CT feature analysis for diagnosing the pathological types of pulmonary nodules
OBJECTIVES: Accurate preoperative classification of pulmonary nodules (PNs) is critical for guiding clinical decision-making and preventing overtreatment. This study aims to evaluate the predictive performance of artificial intelligence (AI)-based quantitative computed tomography (CT) feature analysis in differentiating among four pathological types of PNs: atypical adenomatous hyperplasia and adenocarcinoma in situ (AAH + AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IAC), and lung inflammatory nodules (IN). MATERIALS AND METHODS: A total of 462 pathologically confirmed PNs were analyzed. Radiomic features, including CT attenuation metrics, 3D morphometrics, and texture parameters such as entropy and skewness, were extracted using a deep learning-based AI platform. Logistic regression models were constructed using both single- and multi-variable strategies to evaluate the classification accuracy of these features. Moreover, the inclusion of IN as a separate category significantly enhanced the clinical utility of AI in differentiating benign mimickers from malignant nodules. The combined model, which integrated AI-derived features with traditional CT signs, was used to assess the diagnostic performance of the radiomic features in differentiating the four pathological types of nodules. RESULTS: The combined model demonstrated superior diagnostic performance, with area under the curve (AUC) values of 0.936 for IAC, 0.884 for AAH + AIS, and 0.865 for IN. Although MIA showed lower classification accuracy (AUC = 0.707), key features such as entropy, solid component ratio, and total volume effectively distinguished invasive from non-invasive lesions. CONCLUSION: These findings highlight the potential of AI-enhanced radiomics for supporting non-invasive, objective, and individualized diagnosis of PNs. KEY POINTS: Question Can artificial intelligence (AI)-based quantitative CT analysis reliably differentiate benign inflammatory nodules from the spectrum of lung adenocarcinoma subtypes, a common diagnostic challenge? Findings An integrated model combining AI-driven radiomic features and traditional CT signs demonstrated high accuracy in differentiating invasive adenocarcinoma (AUC = 0.936), pre-invasive lesions (AUC = 0.884), and inflammatory nodules (AUC = 0.865). Clinical relevance AI-enhanced radiomics provides a non-invasive, objective tool to improve preoperative risk stratification of pulmonary nodules, potentially guiding personalized management and reducing unnecessary surgeries for benign inflammatory lesions that mimic malignancy.
4 Apr 2026
Read appraisal →Physiological measurement
StructEIT: realistic 3D EIT model generation from CT scans for deep learning applications
Objective.Artificial intelligence has significantly improved image reconstruction quality across various medical imaging modalities. However, its application in electrical impedance tomography (EIT) reconstruction remains limited, mainly due to the absence of comprehensivein vivodatasets that incorporate realistic anatomical geometries and conductivity distributions. This limitation constrains the development of supervised and data-driven reconstruction methods.Approach.To address this bottleneck, we developed StructEIT, an integrated EIT modeling framework for generating anatomically and biophysically realistic EIT simulation models. The framework incorporates three key components: (1) a structure extraction module, which automatically processes human CT scans to extract body contours and organ boundaries, thereby providing high-fidelity spatial geometry for three-dimensional finite element modeling; (2) a surface electrode attachment module, which enables flexible and accurate placement of electrodes on irregular body surfaces, supporting diverse configurations and ensuring precise definition of the electrode-tissue interface; and (3) a tissue property assignment module, which establishes frequency-dependent conductivity models for multiple organs, enabling physiologically realistic conductivity values across tissues.Main results and Significance.By bridging the gap between CT imaging and EIT, StructEIT facilitates flexible, realistic, and scalable generation of high-resolution EIT datasets. Using this framework, we constructed Chest-EIT, a thoracic EIT simulation dataset comprising over 1400 publicly available CT cases, with multiple electrode configurations provided for each case.
4 Apr 2026
Read appraisal →Annals of oncology : official journal of the European Society for Medical Oncology
Deep learning discriminates thymic epithelial tumors' histological subtypes using digital pathology.
BACKGROUND: Thymic epithelial tumors (TETs) are rare malignancies that pose significant diagnostic challenges due to their heterogeneous histological patterns and substantial interobserver variability in classification. Despite standardized World Health Organization (WHO) classification criteria, diagnostic concordance remains suboptimal, particularly in nonexpert settings, where second-opinion reviews lead to diagnostic reclassification in up to 57% of cases. Deep learning may offer a tool to reduce diagnostic variability and improve the consistency of histological classification. MATERIALS AND METHODS: We trained a deep learning-based model using hematoxylin-eosin (H&E) whole-slide images from The Cancer Genome Atlas as a training dataset. The model incorporated a novel hierarchical loss function designed to reflect clinically relevant tumor groupings based on treatment strategies and patient outcomes. We validated the model on 112 consecutive cases from the University of Chicago, with diagnoses confirmed by an expert thoracic pathologist. Model performances were evaluated using both a three-group hierarchical scheme and the six-class WHO classification. RESULTS: In the clinically relevant hierarchical three-group classification (As: A + AB; Bs: B1 + B2 + B3; and thymic carcinoma), the model achieved an accuracy of 91.1% with Cohen's κ = 0.859, indicating almost perfect agreement. In the six-class classification (A, AB, B1, B2, B3, and thymic carcinoma), the accuracy was 77.7% with κ = 0.716. The model demonstrated 100% sensitivity and 94.6% accuracy for thymic carcinoma detection. Notably, 60% of misclassifications occurred within the same clinical management group, thereby limiting their impact on therapeutic decision making. CONCLUSION: This deep learning model demonstrates strong potential as a diagnostic tool for TETs classification, particularly in settings with limited thoracic pathology expertise. The high sensitivity for thymic carcinoma detection and robust performance across different tissue processing conditions suggest its clinical applicability for improving diagnostic consistency and supporting pathological decision making in both specialized and nonspecialized settings.
4 Apr 2026
Read appraisal →Annals of oncology : official journal of the European Society for Medical Oncology
Next-generation multicenter studies: using artificial intelligence to automatically process unstructured health records of patients with lung cancer across multiple institutions
BACKGROUND: Manual abstraction of real-world data (RWD) from unstructured health records (HRs) remains resource intensive, error prone, and highly variable across institutions. Large language models (LLMs) offer a scalable alternative, but their performance in multicenter oncology settings is not fully validated. PATIENTS AND METHODS: We conducted a multicenter study within the French Large & Unified Cancer Cohort (LUCC) consortium to compare the accuracy of artificial intelligence (AI)-based data extraction against manual abstraction by clinical research professionals. A fine-tuned LLM was applied to de-identified unstructured HRs in PDF format to extract 31 variables from lung cancer patients across 10 centers. Ground truth was defined as concordant values across sources, with discrepant cases adjudicated by a blinded expert. The primary endpoint was the extraction error rates. Secondary endpoints included per-variable performance, interinstitutional variability, F1-score for multiple-choice variables, added value of hybrid AI-human workflows, and survival analyses. RESULTS: Among 10 327 patients with AI-based extraction, 311 were included in the test cohort. Across 8708 datapoints for 28 variables with only one correct answer, the LLM achieved a 7.0% error rate, outperforming manual abstraction (14.2%, P <0.001). The F1-scores of three multiple-choice variables were superior (gene alterations 0.97 versus 0.86, comorbidities 0.86 versus 0.76, metastatic sites 0.71 versus 0.69). Interinstitutional variance was lower with AI (0.12% versus 0.39%). A hybrid approach with targeted human review of 30% of low-confidence AI outputs further decreased error rates to 4.4%. Survival analyses based on AI-extracted data closely matched ground truth, with similar median overall and progression-free survival. CONCLUSIONS: In a multicenter setting, our AI pipeline yielded lower error rates and greater consistency than manual abstraction. These findings support the feasibility of next-generation, AI-enabled multicenter studies to generate high-quality RWD at scale, with potential applicability in prospective clinical trials.
4 Apr 2026
Read appraisal →Respiratory medicine
A review of artificial intelligence-based research on chronic obstructive pulmonary disease.
In recent years, with the rapid development of artificial intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), one of the world's three major chronic diseases, has achieved remarkable progress in diagnosis, grading, and prognosis, which is of great significance for promoting the clinical transformation of respiratory diseases. To deeply explore the application of AI in the diagnosis and management of COPD, this paper reviews recent studies based on machine learning and deep learning, covering screening and diagnosis, disease grading and assessment, disease management and monitoring, and treatment. First, the technical basis of COPD-related research is analyzed from five perspectives: traditional research methods, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Then, the commonly used datasets and model evaluation metrics are summarized. Finally, the application scenarios of AI in COPD research are elaborated, focusing on three aspects: early screening and diagnosis, disease monitoring and risk prediction, and disease classification and risk stratification. This paper summarizes the main research hotspots at home and abroad over the past five years with representative models, and analyzes and compares the advantages and limitations of each type of model in specific COPD tasks through comparative experiments. The study also outlines prospects for the future development of this field, aiming to provide theoretical references and insights for subsequent research.
4 Apr 2026
Read appraisal →J Allergy Clin Immunol
Proteomic profiling and machine learning for endotype prediction in chronic rhinosinusitis
BACKGROUND: Chronic rhinosinusitis (CRS) is a common, heterogeneous upper airway inflammatory disorder, affecting approximately 12% of the general population. The disease is clinically stratified into CRS without nasal polyps and CRS with nasal polyps, including the most severe subtype of nonsteroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (N-ERD). OBJECTIVE: To identify molecular signatures and biomarkers allowing for the distinction between different disease endotypes and controls, we used targeted proteomics combined with bioinformatics and machine learning analyses. METHODS: Nasal secretions and serum from 80 patients (20 each of CRS without nasal polyps, CRS with nasal polyps, N-ERD, and disease controls) were subjected to high-throughput targeted proteomics (Olink). The expression patterns of 161 and 2677 proteins, for nasal secretions and serum, respectively, were analyzed alongside clinical evaluations of nasal polyp and smell test scores. RESULTS: Two distinct expression patterns were identified in nasal secretions: proteins associated with macrophage recruitment and type 2 inflammation were increased in CRS with nasal polyps and N-ERD, whereas proteins associated with innate immunity, particularly Toll-like receptor 4 signaling, were gradually downregulated from disease control to N-ERD. Furthermore, using machine learning, we confirmed 2 potential biomarkers for nasal polyposis: the glial cell line-derived neurotrophic factor in nasal secretions and Charcot-Leyden crystal protein in serum. CONCLUSIONS: Our findings provide unique insights into CRS pathophysiology and highlight potential biomarkers for precision diagnosis and treatment, particularly in severe cases such as N-ERD.
1 Jan 2026
Read appraisal →Curr Opin Pulm Med
Foreign body removal
PURPOSE OF REVIEW: Airway foreign body aspiration remains a potentially life-threatening emergency, predominantly affecting children under 5 years and adults over 65 years. This review synthesizes current evidence on diagnostic strategies, bronchoscopic extraction techniques, procedural outcomes, complication management, and emerging technologies in airway foreign body management. RECENT FINDINGS: Multidetector computed tomography with three-dimensional reconstruction has significantly improved diagnostic accuracy, achieving sensitivity of 98-99% for radiopaque objects and 85-92% for radiolucent materials. Flexible bronchoscopy has evolved from a diagnostic tool to a first-line therapeutic modality, with recent pediatric meta-analyses demonstrating 87% success rates and adult series showing comparable outcomes to rigid bronchoscopy for appropriately selected cases. Rigid bronchoscopy maintains superiority in asphyxiating presentations, and for large (>1.5 cm), sharp, or severely impacted foreign bodies. Technological innovations including robotic-assisted bronchoscopy, electromagnetic navigation systems, and artificial intelligence-powered imaging analysis are enhancing procedural precision and safety. SUMMARY: Successful airway foreign body management requires individualized, multidisciplinary approaches integrating patient clinical status, foreign body characteristics, and institutional expertise. Success depends on appropriate bronchoscopic modality selection, comprehensive preprocedural planning, availability of specialized retrieval instruments, and readiness to manage potential complications. Integration of advanced imaging, simulation-based training protocols, and telemedicine consultation are becoming essential components of contemporary practice.
1 Jan 2026
Read appraisal →Cell
Generation of antigen-specific paired-chain antibodies using large language models.
The traditional process of antibody discovery is limited by inefficiency, high costs, and low success rates. Recent approaches employing artificial intelligence (AI) have been developed to optimize existing antibodies and generate antibody sequences in a target-agnostic manner. In this work, we present MAGE (monoclonal antibody generator), a sequence-based protein language model (PLM) fine-tuned for the task of generating paired human variable heavy- and light-chain antibody sequences against targets of interest. We show that MAGE can generate novel and diverse antibody sequences with experimentally validated binding specificity against SARS-CoV-2, an emerging avian influenza H5N1, and respiratory syncytial virus A (RSV-A). MAGE represents a first-in-class model capable of designing human antibodies against multiple targets with no starting template.
1 Dec 2025
Read appraisal →J Allergy Clin Immunol
Explainable artificial intelligence predicts inflammatory and spatial heterogeneity from nasal polyp histology
BACKGROUND: Chronic rhinosinusitis with nasal polyps is a heterogeneous disorder characterized by diverse inflammatory signatures and endotypes. OBJECTIVE: We sought to develop a histology-based deep learning network for predicting inflammatory gene signatures and spatial patterns in chronic rhinosinusitis with nasal polyps. METHODS: We developed HE2Signature, a deep learning model, using 70 hematoxylin and eosin-stained whole-slide images of nasal polyps paired with corresponding endotypic signature gene expression profiles derived from transcriptomic data. The model was validated in an internal cohort (n = 30) and tested in an independent external cohort (n = 224) from 4 medical centers. The performance was evaluated using correlation and confusion matrix analyses and receiver-operating characteristic curves. Spatial predictions were validated by immunohistochemistry. RESULTS: The HE2Signature demonstrated strong positive correlations between the predicted and actual expression levels of 28 of the 33 signature genes. The internal validation cohort was stratified into distinct endotypes on the basis of predicted signature gene expression, achieving area under the curve values of 0.833, 0.903, and 0.935 for the T1, T2, and T3 endotypes, respectively. In the external cohort, the combination of HE2Signature-predicted Fc epsilon receptor II and CST1 effectively identified the T2 endotype, with a receiver-operating characteristic value of 0.716. Histopathologic examination of high-prediction patches revealed characteristic features associated with signature gene expression. Heatmaps and immunohistochemistry confirmed the accuracy of the model in mapping spatial gene expression patterns. HE2Signature-predicted spatial expression of signature genes correlated with blood and tissue eosinophil counts, Lund-Kennedy score, and Sino-Nasal Outcome Test-22 score, particularly within subepithelial regions. CONCLUSIONS: Our study introduces the first histology-based, explainable deep learning model capable of predicting inflammatory gene signatures and spatial molecular heterogeneity. This proof-of-concept highlights that artificial intelligence-powered histopathologic analysis can generate digital biomarkers by linking tissue patterns with molecular profiles, providing a clinically applicable framework for endotype-guided precision medicine in chronic rhinosinusitis with nasal polyps.
1 Dec 2025
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